Download Fractionation of social brain circuits in autism

doi:10.1093/brain/aws160
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BRAIN
A JOURNAL OF NEUROLOGY
Fractionation of social brain circuits in autism
spectrum disorders
Stephen J. Gotts,1 W. Kyle Simmons,2 Lydia A. Milbury,1 Gregory L. Wallace,1
Robert W. Cox3 and Alex Martin1
1 Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health (NIMH), National Institutes of
Health, Bethesda, MD 20892, USA
2 Laureate Institute for Brain Research, Tulsa, OK 74136, USA
3 Scientific and Statistical Computing Core, National Institute of Mental Health (NIMH), National Institutes of Health, Bethesda, MD 20892, USA
Correspondence to: Stephen J. Gotts,
Section on Cognitive Neuropsychology,
Laboratory of Brain and Cognition,
National Institute of Mental Health (NIMH),
National Institutes of Health, Bethesda, MD 20892, USA
E-mail: [email protected]
Autism spectrum disorders are developmental disorders characterized by impairments in social and communication abilities and
repetitive behaviours. Converging neuroscientific evidence has suggested that the neuropathology of autism spectrum disorders
is widely distributed, involving impaired connectivity throughout the brain. Here, we evaluate the hypothesis that decreased
connectivity in high-functioning adolescents with an autism spectrum disorder relative to typically developing adolescents is
concentrated within domain-specific circuits that are specialized for social processing. Using a novel whole-brain connectivity
approach in functional magnetic resonance imaging, we found that not only are decreases in connectivity most pronounced
between regions of the social brain but also they are selective to connections between limbic-related brain regions involved in
affective aspects of social processing from other parts of the social brain that support language and sensorimotor processes. This
selective pattern was independently obtained for correlations with measures of social symptom severity, implying a fractionation
of the social brain in autism spectrum disorders at the level of whole circuits.
Keywords: autism spectrum disorders; functional connectivity; resting state functional MRI; limbic system; cluster analysis
Abbreviations: ADI = autism diagnostic interview; ADOS = autism diagnostic observation schedule
Introduction
Autism spectrum disorders are developmental disorders characterized by impairments in social and communication abilities, along
with restricted interests and repetitive behaviours. Converging
neuroscientific evidence has shown that autism spectrum disorder-related neuropathology is not localized to one brain region
but involves brain networks over a larger spatial scale, suggesting
that autism spectrum disorders reflect alterations in connectivity at
the level of whole brain circuits (Castelli et al., 2002; Belmonte
et al., 2004; Just et al., 2004). Consistent with this, evidence from
neuroimaging studies of anatomical connectivity using diffusion
tensor imaging and ‘functional’ connectivity that examine patterns
of correlated activity between brain regions have shown reduced
connectivity in autism spectrum disorders over a wide range of
ages from toddlers to adults and involving a variety of brain
regions in frontal, temporal and parietal cortex (Booth et al.,
2011; Müller et al., 2011; Vissers et al., 2012, for recent reviews).
Received January 3, 2012. Revised May 3, 2012. Accepted May 7, 2012. Advance Access publication July 11, 2012
Published by Oxford University Press on behalf of the Guarantors of Brain 2012.
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Figure 1 Areas of the ‘social brain’. A set of brain regions are commonly co-activated across a range of social tasks: the medial and
ventromedial prefrontal cortex, the posterior cingulate/precuneus, the amygdala and anterior hippocampus, the anterior temporal lobes,
the posterior superior temporal sulcus and temporo-parietal junction, the lateral portion of the fusiform gyrus, the left inferior frontal
gyrus, somatosensory and anterior intraparietal cortices, and the anterior insula (not shown). These are often referred to collectively as the
‘social brain’ (for reviews, see Frith and Frith, 2007; Olson et al., 2007; Blakemore, 2008; Adolphs, 2009; Mitchell, 2009).
Parallel research in social neuroscience has revealed a set of
brain regions that appear to be selectively involved in social processing in typically developing humans, as well as in other social
animal species such as monkeys (Frith and Frith, 2007; Adolphs,
2009; Mitchell, 2009; Sallet et al., 2011) (Fig. 1). These include
classic limbic areas (e.g. amygdala: Adolphs and Spezio, 2006;
Adolphs, 2010; and anterior hippocampus: Fanselow and Dong,
2010) and related cortex such as the more ventral and medial
aspects of prefrontal cortex (Carmichael and Price, 1995;
Amodio and Frith, 2006; Frith, 2007) and the anterior temporal
lobes (Olson et al., 2007; Saleem et al., 2008; Simmons and
Martin, 2009, 2011), the posterior cingulate and precuneus
(Cavanna and Trimble, 2006; Andrews-Hanna et al., 2010b), posterior temporal regions involved in representing form, motion and
conceptual information about animate entities (lateral fusiform
gyrus: Kanwisher et al., 1997; Chao et al., 1999; Wiggett et al.,
2009; posterior superior temporal sulcus and temporo-parietal
junction: Castelli et al., 2002; Beauchamp et al., 2003; Samson
et al., 2004; Deen and McCarthy, 2010), the left inferior frontal
gyrus involved in social communication (for review, see Turken
and Dronkers, 2011), as well as somatosensory and anterior intraparietal cortices involved in action understanding (Hamilton and
Grafton, 2006; Adolphs, 2009) and parts of the insula involved in
representing visceral/emotive responses to social stimuli (Singer
et al., 2004; von dem Hagen et al., 2009). The idea of the
‘social brain’, originally proposed based on studies in monkey
(Brothers, 1990), has gained increasing support based on the
common co-activation of these areas during performance of a
wide range of social tasks (Frith and Frith, 2007; Blakemore,
2008; Adolphs, 2009; Mitchell, 2009).
A basic question arises from a joint examination of this literature: To what extent is abnormal connectivity in autism spectrum
disorders limited to the ‘social brain’? The phenotype among
individuals diagnosed with an autism spectrum disorder can vary
considerably, including co-morbid general intellectual impairments,
epilepsy and/or known genetic disorders (Jeste, 2011). However,
pronounced social impairments are common to all of these
individuals. If one were to consider a relatively homogenous subsample of high-functioning [i.e. intelligent quotient (IQ) scores in
the average or above average range] individuals with an autism
spectrum disorder without the presence of additional neurological
conditions or known genetic disorders, then proponents of the
‘social brain’ should predict: (i) abnormal connectivity will preferentially involve social brain areas and (ii) the magnitude of deviation from control levels of connectivity in these brain areas should
predict the severity of social symptoms measured behaviourally.
On the other hand, proposals that are focused instead on how
connectivity problems can affect information processing more generally would be equally compatible with finding effects outside of
the social brain. Two such prominent proposals include a disconnection between the hemispheres via the corpus callosum, with
further disconnection between frontal and more posterior brain
regions (Just et al., 2007; Schipul et al., 2011), as well as the
proposal that strong but non-selective local connectivity leads to
Fractionation of social brain circuits
decreases in long-range connectivity (Belmonte et al., 2004; see
also Markram and Markram, 2010; Vattikuti and Chow, 2010).
The extent to which abnormal connectivity in autism spectrum
disorders is limited to domain-specific social brain areas has not
been directly evaluated to date. In part, this has been due to methodological limitations. Connectivity differences in functional MRI,
assessed using correlation or related methods, are much more difficult to measure in a whole-brain manner than differences in mean
activity level because these methods operate over pairs of voxels or
larger regions of interest. The problem of searching systematically
through all possible combinations of voxels/regions of interest,
along with the accompanying problem of adjusting the statistics
for multiple comparisons, renders systematic examination of connectivity differences between groups impractical if not impossible
(however, cf. Anderson et al., 2011b). In the face of this complexity, most researchers have anchored their correlation analyses
around a small number of ‘seed’ regions of interest. Although the
choice of regions of interest can arguably be determined in reasonable ways (e.g. using separate functional localizer scans or standard
comparisons of group/condition means in task-based connectivity
studies), it is still unclear how comprehensively these regions of
interest will represent the full set of connectivity differences that
might be present in the data. This situation can be influenced by
the choice of stimuli and tasks and potentially by the presence of
overall performance differences on those tasks.
Recently, a number of studies have analysed connectivity differences between subjects with autism spectrum disorders and typically developing control subjects in the absence of an overt task,
while subjects were at rest (e.g. Müller et al., 2011). In
resting-state functional connectivity studies (for review, see Fox
and Raichle, 2007), correlations across brain regions reflect the
covariation of spontaneous or internally generated brain activity.
Spatially specific patterns of correlation in such studies are
strongly, although not exclusively, influenced by anatomical connectivity (Honey et al., 2009) and exist independently of explicit
thought in so far as they can be observed in anaesthetized monkeys and humans during sleep (Fransson et al., 2007; Vincent
et al., 2007; Larson-Prior et al., 2009; Margulies et al., 2009).
However, like task-based functional connectivity studies,
resting-state studies of autism spectrum disorders have only evaluated a handful of seed locations in any individual study, such as
the posterior cingulate cortex (Kennedy and Courchesne, 2008;
Monk et al., 2009; Weng et al., 2010), medial prefrontal cortex
and left angular gyrus (Kennedy and Courchesne, 2008), anterior
versus posterior insula (Ebisch et al., 2011), left intraparietal
sulcus, right superior precentral sulcus and left posterior middle
temporal cortex (Kennedy and Courchesne, 2008; Ebisch et al.,
2011), and structures in the striatum (caudate and putamen,
Di Martino et al., 2011). Several studies have demonstrated
reduced correlation in autism spectrum disorders among regions
of the so-called ‘default’ network (Kennedy and Courchesne,
2008; Monk et al., 2009; Assaf et al., 2010; Weng et al.,
2010), which overlap largely, but not completely, with social
brain regions (Simmons and Martin, 2011). These findings are
consistent with the predictions of the social brain outlined
above. However, the use of such a small number of seed locations, 16 in total in the above studies and no more than seven in
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any individual study, has left us with a piecemeal understanding of
the full pattern of connectivity differences. For comparison, the
number of possible seed locations in a typical whole-brain functional MRI scan is 450 000 when considering single voxels and
41500 when considering non-overlapping spherical regions of
interest with a standard 6-mm radius.
In this study, we combined resting-state functional connectivity
with a novel, data-driven analysis approach that permits a comprehensive, whole-brain characterization of the altered functional
connectivity in high-functioning participants with an autism spectrum disorder. This approach allows us to address two predictions
of the domain-specific neural systems view of social processing:
(i) differences in functional connectivity should be concentrated
preferentially among social brain areas and (ii) the severity of
autism spectrum disorder social symptoms should be predicted
preferentially by connectivity levels among social brain areas.
Materials and methods
Participants
Twenty-nine typically developing participants (28 males, one female)
between 12 and 23 years of age, and 31 high-functioning participants
(29 males, two females) with an autism spectrum disorder, between
12 and 23 years of age, took part in the study. Participants with
autism spectrum disorders were recruited from the Washington, DC,
metropolitan area and all met Diagnostic and Statistical Manual-IV
diagnostic criteria as assessed by an experienced clinician (20
Asperger’s syndrome, seven high-functioning autism and four pervasive developmental disorder-not otherwise specified). Thirty participants with autism spectrum disorders received the autism diagnostic
interview (ADI or ADI-R) (Le Couteur et al., 1989; Lord et al., 1994)
and the autism diagnostic observation schedule (ADOS, modules 3 or
4; Lord et al., 2000), administered by a trained, research-reliable clinician. All scores from participants with autism spectrum disorders met
cut-off for the category designated as ‘broad autism spectrum disorders’ according to criteria established by the National Institute of
Child Health and Human Development/National Institute on
Deafness and Other Communication Disorders Collaborative Programs
for Excellence in Autism (Lainhart et al., 2006). As the ADI and ADOS
do not provide an algorithm for Asperger’s syndrome, Lainhart et al.
(2006) developed criteria that include an individual on the broad
autism spectrum if she/he meets the ADI cut-off for ‘autism’ in the
social domain and at least one other domain, or meets the ADOS
cut-off for the combined social and communication score. Additionally,
to be conservative, group comparisons were reanalysed using only
participants with autism spectrum disorders who met research criteria
on the ADOS (27 of 31 participants with autism spectrum disorders’
ADOS combined social and communication scores met the ‘autism
spectrum disorders’ cut-off of 7). The results from this additional analysis were comparable with the results for the entire sample of 31
participants with autism spectrum disorders. Scores on the Social
Responsiveness Scale (Constantino, 2002), an informant-based rating
scale used to assess autism spectrum disorders social and communication traits quantitatively over the full range of severity, were obtained
from parents for 29 participants with autism spectrum disorders. IQ
scores were obtained for all participants, and all full-scale IQ scores
were 585 as measured by the Wechsler Abbreviated Scale of Intelligence (26 with autism spectrum disorders, 29 typically developing),
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Table 1 Demographic characteristics of study participants
Age
Full scale IQ
Sex (male:female)
ADI social
ADI communication
ADI-restricted/repetitive
behaviours
ADOS communication + social
interaction
Social Responsiveness
Scale total score
Autism spectrum
disorders
Typically
developing
16.92
112.87
29:2
19.53
14.73
5.67
17.86 (3.00)
114.17 (10.43)
28:1
(2.66)
(15.81)
(4.99)
(4.27)
(2.63)
11.47 (4.62)
82.53 (29.09)
Data are mean (SD).
the Wechsler Adult Intelligence Scale-III (three with autism spectrum
disorders), or the Wechsler Intelligence Scale for Children-IV (two with
autism spectrum disorders). Participant groups did not differ in terms
of full-scale IQ, age, or sex ratio (Table 1). Informed assent and consent were obtained from all participants and/or their parent/guardian
when appropriate in accordance with a National Institutes of Health
Institutional Review Board approved protocol.
Functional magnetic resonance imaging
Functional MRI data were collected using a GE Signa 3 T whole-body
MRI scanner at the NIH Clinical Centre NMR Research Facility using
standard imaging procedures. For each participant, a high-resolution
T1-weighted anatomical image (MPRAGE) was obtained (124 axial
slices, 1.2 mm slice thickness, field of view = 24 cm, 224 224 acquisition matrix). Spontaneous, slowly fluctuating brain activity was
measured during functional MRI using a gradient-echo echo-planar
series with whole-brain coverage while participants maintained fixation
on a central cross and were instructed to lie still and rest quietly (repetition time = 3500 ms, echo time = 27 ms, flip angle = 90 , 42 axial contiguous interleaved slices per volume, 3.0 mm slice thickness, field of
view = 22 cm, 128 128 acquisition matrix, single-voxel volume =
1.7 mm 1.7 mm 3.0 mm). Each resting scan lasted 8 min, 10 s for
a total of 140 consecutive whole-brain volumes. Independent measures of nuisance physiological variables (cardiac and respiration) were
recorded during the resting scan for later removal. A GE 8-channel
send-receive head coil was used for all scans, with a SENSE factor of 2
used to reduce gradient coil heating during the session.
Functional magnetic resonance imaging
preprocessing
Prior to statistical analyses, image preprocessing was conducted using
the AFNI software package (Cox, 1996). The first four echo-planar
image volumes were removed from the resting scan and large transients in the remaining volumes were removed through interpolation
(using AFNI’s 3dDespike). Volumes were then slice-time corrected,
co-registered to the anatomical scan, resampled to 2.0 mm isotropic
voxels, smoothed with an isometric 6-mm full-width half-maximum
Gaussian kernel, normalized by the mean signal intensity in each
voxel to reflect per cent signal change, and transformed into the
standardized Talairach and Tournoux (1988) volume for the purposes
of group analyses. We applied the basic ANATICOR procedure
(Jo et al., 2010) for removing nuisance physiological and non-physiological artefacts from the echo-planar image data as follows: the
anatomical scan was segmented into tissue compartments using
Freesurfer (Fischl et al., 2002). Ventricle and white-matter masks
were created, eroding the white matter masks to prevent partial
volume effects with grey matter. Masks were then applied to the
volume-registered echo-planar image data prior to smoothing to
yield pure nuisance time series for the ventricles, as well as local estimates of the blood oxygen level-dependent signal in white matter that
were averaged within a 15-mm radius sphere. Retroicor (Glover et al.,
2000) and respiration volume per time (Birn et al., 2008) regressors
were created from the cardiac and respiration measures. All nuisance
time series were detrended with fourth-order polynomials prior to
least-squares model fitting to each voxel’s time series. Nuisance variables for each voxel included the following: an average ventricle time
series, a local average white-matter time series, six parameter estimates for head motion and nine respiration regressors from
Retroicor and respiration volume per time (for slice time 0, applied
after slice-time correction; Supplementary Fig. 1). The predicted time
course of these nuisance variables was then subtracted from the full
voxel time series to yield a residual time series to be used in correlation
analyses. Large artefacts in the cardiac measures were present in most
participants due to poor skin contact, preventing their inclusion. Global
brain signal was also not included among the nuisance variables, as
whole-brain correlation analyses revealed substantial group-level differences in global signal distribution (Supplementary Fig. 2). Removing
global signal would therefore be expected to alter the group-level
analyses qualitatively, introducing group differences where none
existed in the original data (Murphy et al., 2009; Fox and Greicius,
2010; Saad et al., 2012).
Functional magnetic resonance imaging
analyses
Following functional MRI preprocessing and the removal of nuisance
variables, brain masks were created for each participant that excluded
the ventricles, white matter, and other non-brain tissue (Fig. 2A). Then
functional ‘connectedness’ maps were created by calculating the correlation (Pearson’s r) of the residual time series in each voxel with
every other voxel in their brain mask and storing the mean correlation
back in the voxel (using the AFNI function 3dTcorrMap). These connectedness values were then transformed using Fisher’s z to yield normally distributed values. Group mean connectedness was then
compared between the autism spectrum disorders and typically
developing groups using two-sample t-tests (two-tailed) in each
voxel in Talairach coordinates to identify candidate seeds. Only
voxels present in 485% of single-participant brain masks in both
the autism spectrum disorders and typically developing groups were
included in the group analyses to prevent any potential gross differences in brain anatomy from biasing the results. Clusters of voxels with
a significant t-value (P 5 0.05, uncorrected) and at least 100 contiguous voxels in the brain volume then served as seed regions of interest
(a total of 13) for the next step of the analysis. The rationale for
choosing P 5 0.05 (uncorrected) at this early step was to be as inclusive as possible in considering candidate seeds (with a cluster-size criterion of 100 voxels intended to reduce the number of Bonferroni
corrections that would be necessary at the next analysis step). We
also considered it likely that comparing whole-brain connectedness
would dilute or attenuate the magnitude of correlation differences,
because these comparisons are intrinsically averaged across correlated
and uncorrelated locations. The seeds for the left hippocampus and
Fractionation of social brain circuits
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Figure 2 Using ‘connectedness’ to identify seed regions of interest that yield differences in functional connectivity between autism
spectrum disorders (ASD) and typically developing (TD) groups. For each participant, in A, functional connectedness was calculated for
each voxel within the brain mask by first extracting its specific time series (i.e. the ‘seed’), then correlating it with all of the other voxels’
time series. These correlation values were then averaged over the brain mask, storing the mean value back into the seed voxel, and this
process was then repeated for all voxels. For group analyses, in B, individual connectedness maps were transformed to normally distributed
values (Fisher’s z) and averaged by group (autism spectrum disorders versus typically developing) (shown are the group-average ztransformed connectedness values, Rz0 ; see colour bar). (C) The normally distributed group maps could then be compared using t-tests
(P 5 0.05, two-tailed, minimum cluster size = 100 contiguous voxels). Data for this and subsequent figures are plotted in standard
anatomical coordinates (Talairach-Tournoux).
amygdala were originally contiguous, and given the prior empirical
evidence for abnormality of both structures in autism spectrum disorders (Schumann et al., 2004; Schumann and Amaral, 2006), these
were broken into two regions of interest to evaluate their separate
contributions by first making an independent anatomical mask of the
left amygdala from a Talairach version of MNI group brain template
(AFNI TT_N27 brain). Voxels that were part of the original cluster but
lay outside of the amygdala seed region of interest served as the
hippocampus seed region of interest. Prior to seed testing, the reliability of the connectedness comparisons was evaluated by breaking each
participant’s data into two halves (first half = 68 time points; second
half = 68 time points) and then randomly assigning these halves in a
counterbalanced manner across participants into two new data sets,
each with an equal number of first and second half segments.
Connectedness was recalculated separately for both of these data
sets and compared through t-tests in the same manner. Voxels for
which typically developing 4 autism spectrum disorders was significant
in both halves at P 5 0.05 (both one- and two-tailed; Supplementary
Fig. 3) were then identified. All 13 seeds showed good agreement
across the two halves of data in this manner (compare Supplementary
Fig. 3 with Fig. 3).
Correlation maps were created for each of the 13 seeds and
for each participant, transformed using Fisher’s z, and then
compared with additional two-sample t-tests (two-tailed) at
P 5 0.001, correcting for whole-brain comparisons by cluster size
(using AFNI’s AlphaSim) and the number of seed tests (Bonferroni)
to P 5 (0.05/13) (cluster size threshold = 464 mm3). Lieberman and
Cunningham (2009) have suggested that voxel-wise alpha levels of
P 5 0.005 result in a good trade-off of type I and type II statistical
errors in functional MRI research, and the group comparisons reported
in this study are qualitatively similar using either P 5 0.001 or
P 5 0.005 (results not shown). The Bonferroni corrections applied to
the whole-brain corrected P-value were found to accurately reduce the
expected family-wise type I error of finding even a single chance seed
result to P 5 0.05 when the full set of seed selection criteria were
checked using Monte Carlo simulation. In the corrected
group-difference maps, clusters of voxels (420 contiguous) detected
in more than one seed test, but were distinct from the seed regions of
interest, were combined with seed regions of interest yielding corrected results for region of interest–level group correlation analyses
(a total of 27 regions of interest; Table 2 and Supplementary Fig. 4
for further details). Region X Region correlation matrices were constructed for each participant, averaged by group, and then submitted
to K-means clustering and a metric version of multi-dimensional scaling, both implemented in Matlab (http://www.mathworks.com), to
identify regions of interest with similar patterns of functional connectivity with respect to the other regions of interest (Supplementary material). Group comparisons of functional connectivity could then be
carried out at the cluster level by finding the median Region X
Region correlation both within and between clusters for each participant. Non-parametric permutation tests (Maris and Oostenveld,
2007; Kravitz et al., 2010) were conducted for the cluster-level
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Correlations with autism spectrum
disorders symptom severity
Figure 3 Seed and non-seed regions of interest showing significant differences between the autism spectrum disorders
(ASD) group and the typically developing (TD) group. Seed
regions of interest from Fig. 2 that yielded significant differences
in functional connectivity between groups (all typically developing 4 autism spectrum disorders) are shown in orange.
Contiguous non-seed voxels that appeared in 52 of the individual seed maps were included as additional non-seed regions
of interest (shown in yellow). All results are corrected for multiple comparisons with P 5 0.05 both for whole-brain tests, as
well as the number of seeds tested (see Supplementary Fig. 4
and Table 2 for complete details).
analyses by jointly shuffling rows/columns of the Region X Region
matrix (i.e. if one region of interest was randomly picked to be the
third row, this region of interest also served as the third column),
permitting the same row/column interdependencies in the actual
data to occur in the randomly permuted data. The number of times
that a difference greater than or equal to the magnitude observed in
the actual data occurred in the shuffled data was tabulated, and this
frequency divided by the total number of shuffles (10 000 for these
analyses) corresponded to the likelihood of a Type I error (e.g.
P 5 0.05 corresponded to fewer than 500/10 000 occurrences).
Correlations of autism spectrum disorder symptom severity with
Region X Region functional connectivity and whole-brain functional
connectedness were carried out, removing the effects of age and IQ
using partial correlation. These analyses were statistically independent
of the autism spectrum disorders/typically developing group comparisons because they utilized the variance of Region X Region functional
connectivity measures around the mean solely within the autism spectrum disorders group and did not depend in any way on the mean
differences between the two groups. Measures of symptom severity
were taken from the ADI (social, communication and restricted/repetitive behaviours subscales), ADOS (communication + social interaction
subscale) and Social Responsiveness Scale (total score). Each of these
scales was first correlated with the cluster-level median Region X
Region correlations that were extracted for each of the participants
with an autism spectrum disorder. As Social Responsiveness Scale total
score was significantly correlated with median Region X Region functional connectivity between Cluster 1 and Clusters 2/3, follow-up
whole-brain correlations of Social Responsiveness Scale with the
single-participant connectedness maps were carried out to identify
the brain regions most responsible for driving the significant correlation
with Social Responsiveness Scale. As with the cluster-level analyses,
effects of age and IQ were removed through partial correlation.
Voxel-wise alpha levels of the partial correlation coefficients were
set at P 5 0.01 (although similar results are obtained for P 5 0.05),
corrected for whole-brain comparisons with P 5 0.05 using cluster
size (AFNI AlphaSim) (cluster size threshold = 912 mm3). Less stringent voxel-wise alpha levels were used for the functional MRIbehaviour correlations than for the group comparisons due to the
relatively small number of degrees of freedom (df) available for the
partial correlation analysis [n = 29; df = 25 (N-2-2 covariates)]. For
df = 25, the voxel-level alpha of P 5 0.01 corresponds to a threshold r-value 5 0.49, in the range of functional MRI–behaviour
correlations that are typically reported in social neuroscience studies
(0.4–0.6) (Vul et al., 2009). Both the group comparisons and
correlations with autism spectrum disorder symptom severity were
corrected for multiple comparisons to the same family-wise alpha
(P 5 0.05).
Results
Using functional MRI, we scanned 31 subjects with autism spectrum disorders and 29 typically developing control participants
who were matched on age, IQ and sex. As discussed earlier, we
sought to limit the heterogeneity of our autism spectrum disorders sample and rule out basic confounds of overall intellectual
ability by restricting our study to high-functioning participants
with autism spectrum disorders (IQ 4 85) without epilepsy,
other neurological or known genetic disorders (Table 1). We
examined functional connectivity of slowly fluctuating, spontaneous brain activity while participants were at rest. Rather than
exhaustively testing all possible seed locations, our analysis
method (Fig. 2) employed a simplification that is capable of
yielding the same kind of information. We first assessed the
average level of functional connectivity of each functional MRI
voxel with the rest of the brain for each participant (Fig. 2A)
(see also Buckner et al., 2009; Cole et al., 2010; Salomon et al.,
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Table 2 Regions of interest showing differences in functional connectivity (typically developing 4 autism spectrum
disorders)
Regions of interest in matrix row/column order
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Ventromedial prefrontal
Left amygdala
Left hippocampus
Right ventromedial anterior temporal
Left ventromedial anterior temporal
Right anterior middle temporal
gyrus/superior temporal gyrus
Right dorsomedial frontal pole
Left inferior frontal gyrus (pars triangularis)
Left inferior frontal gyrus (pars orbitalis)
Left inferior frontal gyrus (pars opercularis)
Right inferior frontal gyrus (pars opercularis)
Left inferior temporal gyrus
Left temporo-parietal junction
Left anterior superior temporal
Right superior temporal sulcus
Left anterior superior frontal
Left occipital
Left cuneus
Right precuneus
Left fusiform gyrus
Right posterior middle temporal gyrus/occipital
Right dorsal occipital
Left postcentral gyrus
Right postcentral gyrus
Left supplementary motor area
Right supplementary motor area
Left cerebellum
Talairach coordinates of region of interest ‘peak’
Volume Regions
Overlapping
(mm3) of interest seed maps
with seed (max)
X
Y
Z
3
23
31
21
23
53
27
5
19
1
5
5
16
16
12
36
38
16
3864
1032
1048
1448
1424
1792
9
43
41
41
53
59
47
53
55
23
9
3
15
45
49
23
45
21
3
5
35
61
17
31
15
13
37
59
9
45
41
83
93
63
45
73
75
31
27
3
9
59
16
20
2
2
6
8
12
6
6
24
36
12
54
18
2
28
58
68
64
64
-48
376
424
800
544
496
2208
2016
5424
2280
1360
6176
232
2736
744
10 208
3280
4408
2128
208
296
1856
17
1
11
14
9
6
1
2
10
2
3
13
1
1
1
1
0
2
2
3
3
3
2
2
3
4
5
3
3
2
3
2
6
4
4
2
2
2
0
Regions of interest are listed in order of their row/column presentation in Figs. 4 and 5, where r Regions 1–7 are members of Cluster 1 (red), Regions 8–16 are members of
Cluster 2 (blue), and Regions 17–27 are members of Cluster 3 (green). Bold text indicates that a region of interest was one of the 12 seed regions of interest that produced
significant (corrected) results. Peak Talairach coordinates are for the peak t-value in seed regions of interest for the group comparisons of functional connectedness (Fig. 2),
and for the spatial centroid of non-seed regions of interest. The rightmost two columns show: (i) the total number of regions of interest (out of 27) containing at least one
voxel in the corrected t-test maps for each seed; and (ii) the maximum overlap of the corrected t-test maps for each seed (out of 12) in any single voxel within the region of
interest (Supplementary Fig. 4).
2011). This participant-specific, average ‘connectedness’ measure
can then be averaged over participants within the autism spectrum disorders and typically developing groups and compared
statistically with t-tests after first transforming the correlation
maps to yield normally distributed values (Fig. 2B and C).
Brain locations with low connectedness in one of the two
groups are likely to have corresponding correlation maps that
differ significantly between the groups, allowing us to determine
effective seed locations empirically. Comparison of these connectedness maps led to the identification of 13 seed regions
of interest, all of which showed lower connectedness for the
autism spectrum disorders group relative to the typically
developing group (Fig. 2C) and which were found to be internally consistent when checked with split-half replication
(Supplementary Fig. 3). Of note, with respect to the experimental predictions articulated earlier, nearly all these 13 seed regions
are components of the ‘social brain’ shown in Fig. 1, such as
the ventromedial prefrontal cortex, left anterior hippocampus and
amygdala, bilateral anterior temporal lobes, left temporo-parietal
junction, bilateral postcentral gyrus, right lateral occipital cortex
extending into the right temporo-parietal junction and posterior
middle temporal gyrus. Additional seed regions were identified in
the left posterior inferior temporal gyrus, the posterior parahippocampal gyrus, as well as the cerebellum.
Although this procedure identified seed regions of interest in an
unbiased manner, it did not identify the corresponding brain locations that were driving the differences in connectedness. To evaluate this question, we extracted the voxel-averaged blood oxygen
level-dependent time series for each seed region of interest for
each participant and determined its correlation with all brain
voxels. These resulting correlation maps were then transformed
to normal distributions and compared between the autism spectrum disorders and typically developing groups with t-tests (corrected for multiple comparisons). Twelve of 13 seeds yielded
significant results (orange voxels in Fig. 3; Table 2), with all results
indicating greater functional connectivity for the typically developing group than the autism spectrum disorders group, and no clusters surviving correction that showed the opposite pattern. Only
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the posterior parahippocampal gyrus seed failed to yield corrected
results. An additional control seed placed in the region of the
posterior cingulate cortex typically used to identify the ‘default’
network (Fox et al., 2005) failed to produce significant results at
the same statistical thresholds (Supplementary material). Many of
the 12 remaining seed regions of interest produced results that
overlapped spatially with one another, suggesting circuit-like
interrelationships among the regions of interest. To identify more
completely the brain regions that consistently showed functional
connectivity differences resulting from the seed regions of interest,
we identified voxels that were present in the correlation difference
maps of at least two of the seed regions of interest but were not
seed voxels themselves (Supplementary Fig. 4 and yellow voxels in
Fig. 3). When combined with the 12 significant seed regions of
interest, this yielded a total set of 27 regions of interest showing a
consistent pattern of functional connectivity differences among set
members that distinguished between the autism spectrum disorders and typically developing groups (Table 2).
Organization of region of interest
interrelationships
We visualized and quantified the interrelationships among the 27
regions of interest by first calculating the Region X Region correlation matrices for each single participant and then averaging
across participants within each group to yield group mean
autism spectrum disorders and typically developing matrices. We
then identified regions of interest in the group matrices that had
similar patterns of correlation with the entire set of regions using
multidimensional scaling and K-means cluster analysis. A threecluster solution provided the best trade-off of variance explained
to model complexity according to a simple ‘elbow’ criterion
(Fig. 4A, inset), and the three clusters are shown superimposed
on the multidimensional scaling plot in Fig. 4A using colour.
Cluster 1 (red points in Fig. 4A) corresponded to limbic-related
brain areas that are associated with more affective aspects of
social processing such as ventromedial prefrontal cortex, left
amygdala and the anterior portion of the hippocampus, bilateral
anterior ventral temporal cortex, right anterior middle temporal
gyrus and right anterior medial frontal cortex. Cluster 2 (blue
points) was composed of brain areas involved in linguistic and
communicative aspects of social processing in the left hemisphere
(for review, see Turken and Dronkers, 2011) such as the three
divisions of the inferior frontal gyrus, anterior superior temporal
gyrus, posterior temporal cortex near the temporo-parietal junction, posterior inferior temporal gyrus, as well as left anterior
middle frontal gyrus and right posterior superior temporal sulcus.
Cluster 3 (green points) was composed of brain regions involved in
visuospatial, somatosensory and motor aspects of social processing
in the left extrastriate cortex, bilateral precuneus, left fusiform
gyrus, right middle occipital gyrus, right superior parietal, bilateral
postcentral gyrus, bilateral supplementary motor area and left
cerebellum.
Although all these regions of interest were selected based on
the fact that they showed significant differences in functional connectivity between the two groups, it remained unclear which
S. J. Gotts et al.
combinations of regions showed the largest functional connectivity
differences. One simple approach was to identify which Region X
Region combinations showed differences in functional connectivity
that survived Bonferroni corrections for the number of combinations tested. This is shown in Fig. 4B in the context of an anatomical rendering of the regions of interest in Talairach
coordinates, with region of interest colours matching the cluster
designations in Fig. 4A. Thick lines between two regions of interest
indicate that the correlation values between those two regions of
interest are significantly larger in the typically developing group
than in the autism spectrum disorders group at a level that survives correction. Thin lines indicate a significant difference that
fails to survive correction. There are two striking aspects of the
distribution of the thick lines: (i) they occur almost exclusively
between clusters (23/25 compared to 2/25 within-cluster) and
(ii) virtually all involve Cluster 1 regions of interest (24/25;
Cluster 1 with 2: 9/25, Cluster 1 with 3: 13/25). The region of
interest showing the largest number of Bonferroni-corrected group
differences in functional connectivity was the ventromedial prefrontal region of interest in Cluster 1 (11/25). These results are
clarified further by viewing the full Region X Region correlation
matrices by group but with the rows and columns sorted by cluster (Fig. 4C). The largest differences between the groups occur
between Cluster 1 regions of interest and those in Clusters 2 and
3, with the autism spectrum disorders group showing decreased
correlations between regions (corresponding t-tests shown in
Fig. 4C). In contrast, the group differences among regions of
interest in Cluster 1 and in Clusters 2 and 3 were less pronounced,
with positive correlations observed in both groups at more comparable levels. These issues were evaluated statistically at the cluster level using the median within- versus between-cluster
correlation value for each participant. Group t-tests within and
across clusters revealed differences in functional connectivity between Cluster 1 regions of interest and regions of interest in
Clusters 2 and 3 [Cluster 1 with Cluster 2 regions of interest:
t(df 58) = 4.17, P 5 0.001, two-tailed, Bonferroni-corrected;
Cluster 1 with Cluster 3 regions of interest: t(df 58) = 4.38,
P 5 0.001, two-tailed, corrected], as well as among Cluster 1 regions of interest [t(df 58) = 3.59, P 5 0.01, two-tailed, corrected].
Group differences between Cluster 1 and Clusters 2/3 were also
found to be significantly larger than differences in other portions
of the Region X Region matrix (versus within-Cluster 1, permutation test: P 5 0.05; versus within- and across Clusters 2 and 3,
permutation test: P 5 0.04). Although group differences were
found to be significant among Cluster 1 regions of interest, correlation values were still substantially above chance for the autism spectrum disorders group, indicating that low correlation
values across clusters were not due to the absence of coordinated
activity within Cluster 1 (average median correlation = 0.249,
SEM = 0.011, P 5 10–5; average median correlation for typically
developing group = 0.341, SEM = 0.025, P 5 10–5). Simple alternative explanations of these results in terms of group-level
differences in head motion or local blood oxygen level-dependent
signal amplitudes were evaluated but failed to account for the
observed patterns (Supplementary Figs. 5–11, Supplementary
Table 1). Taken together, these results suggest that not only are
the largest differences in functional connectivity in autism
Fractionation of social brain circuits
Brain 2012: 135; 2711–2725
| 2719
Figure 4 Autism spectrum disorders (ASD) and typically developing (TD) groups differ in the functional relationships among regions of
interest (ROI) at the level of whole circuits. Functional relationships among the regions of interest were visualized both through multidimensional scaling and K-means clustering with good agreement between the methods in A. Points in the 2D scatter plot represent the 27
regions of interest, shown separately for the autism spectrum disorders group and the typically developing group, with near points in the
plot indicating similarity in the pattern of correlation with respect to the entire set of regions of interest. A three-cluster solution (shown in
red, blue and green) served as the best trade-off of variance explained to model complexity (inset), with Cluster 1 (red) identifying
limbic-related regions of interest involved in emotional/affective aspects of social processing, Cluster 2 (blue) identifying regions of interest
related to linguistic aspects, and Cluster 3 (green) identifying regions of interest related to visuospatial and somatomotor aspects. The 3D
anatomical locations of these regions of interest are shown in B by cluster membership in Talairach coordinates. Thick lines connecting
regions of interest correspond to Bonferroni-corrected group differences (t-tests) in the correlation values between those regions of
interest, whereas thin lines denote group differences that failed to survive correction. The full Region X Region correlation matrices are
shown in C for the autism spectrum disorders and typically developing groups, sorted by cluster. The corresponding t-tests, which
highlight the reliable differences between the groups, are shown to the right (see Table 2 for row/column order of region of interest
identities). Correlation values are selectively lower in the autism spectrum disorders group between limbic-related regions of interest in
Cluster 1 and those in Clusters 2 and 3, but are relatively preserved within Cluster 1 (see colour bars for corresponding r- and t-values; red
t-values indicate a Bonferroni-corrected level of significance).
spectrum disorders situated within brain areas that are involved in
aspects of social processing, but also there is a pronounced decoupling of the activity in limbic-related brain regions in Cluster 1
from the activity in other social brain regions.
Correlations with autism spectrum
disorders social behaviours
We next asked whether the functional decoupling of brain regions
in Cluster 1 from Clusters 2 and 3 observed in the autism spectrum disorders/typically developing group differences could
independently predict the severity of the social and communication impairments within the autism spectrum disorders group.
Autism spectrum disorders social symptoms were assessed with
three of the most commonly applied behavioural scales: the
Social Responsiveness Scale, the ADOS and the ADI or ADI-R.
The median Region X Region correlation between Cluster 1 and
Clusters 2 and 3 for each participant with autism spectrum disorders was significantly associated with symptom severity only for
Social Responsiveness Scale (total score) after partialling the effects of age and IQ [partial r (df 25) = -0.39, P 5 0.05], with
weaker across-cluster correlations related to greater severity of
symptoms (Fig. 5A). The full pattern of partial correlations with
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S. J. Gotts et al.
Figure 5 Correlations with autism spectrum disorders (ASD) symptom severity reveal the same circuit-level organization as the group
comparisons. The median correlation value between regions of interest (ROI) in Cluster 1 and those in Clusters 2 and 3 for each participant
with an autism spectrum disorders is negatively related to Social Responsiveness Scale (SRS) total score after controlling age and IQ (lower
median correlation 4 higher severity) is shown in A. The x- and y-axes show adjusted values after removing the linear effects of age and
IQ from each variable using multiple regression. The partial correlation values between functional connectivity (region of interest–region of
interest correlation) and Social Responsiveness Scale total score are shown in B for all region of interest combinations, revealing the mirror
pattern to that observed in C. A thick, dashed line denotes the portion of the matrix used to calculate the median correlations in A. In a
whole-brain search, negative partial correlations of autism spectrum disorders functional connectedness with Social Responsiveness Scale
total score are observed in left ventromedial prefrontal cortex, amygdala and anterior ventral temporal cortex (adjusted for age, IQ) is
shown in C. Results are corrected for multiple comparisons to P 5 0.05 and overlap with three of the seed region of interest identified
independently in the group comparisons (see colour bar for uncorrected significance levels in single voxels).
Social Responsiveness Scale for the entire autism spectrum disorders Region X Region matrix further revealed that symptom
severity was selectively predicted by these particular across-cluster
relationships (Fig. 5B), demonstrating a mirror pattern to that
observed for the group functional differences in Fig. 4C (see
also Supplementary Fig. 12). Indeed, within the autism spectrum
disorders group, partial correlations with Social Responsiveness
Scale were significant for both of the across-cluster relationships
involving Cluster 1 [with Cluster 2: partial r (df 25) = 0.406,
P 5 0.04; with Cluster 3: partial r (df 25) = 0.393, P 5 0.05],
whereas none of the other partial correlations approached significance (P 4 0.2 for all). Although the choice of regions of interest
was determined independently based on the brain regions showing group differences in functional connectivity with the typically
developing participants, we also conducted a whole-brain search
within the autism spectrum disorders group connectedness maps
(Fig. 2B) to determine which brain regions were most responsible
for driving the correlations with Social Responsiveness Scale. Two
clusters of voxels (Fig. 5C), one in the ventromedial prefrontal
cortex and one extending from the left amygdala into the left
anterior ventral temporal cortex, showed significant correlations
with Social Responsiveness Scale after partialling out the effects
of age and IQ and correcting for whole-brain comparisons. These
clusters overlapped substantially with three of the regions of interest in Cluster 1 showing group-level differences between participants with autism spectrum disorders and typically developing
participants (ventromedial prefrontal, left amygdala and left anterior ventral temporal cortex regions of interest; Fig. 4), despite the
fact that they were discovered using only the autism spectrum
disorders group data. As with the region of interest–level analyses,
lower levels of functional connectedness were associated with
higher Social Responsiveness Scale total scores indicating that
decreases in functional connectivity involving these brain areas
predict higher symptom severity in autism spectrum disorders.
Discussion
The domain-specific neural systems view of social processing
produced two central predictions that were evaluated in this
study: (i) differences in interregional correlations between highfunctioning participants with an autism spectrum disorder and
Fractionation of social brain circuits
typically developing participants should be concentrated preferentially among social brain regions and (ii) the severity of autism
spectrum disorders social impairments should be predicted preferentially by correlation levels among social brain regions. Utilizing a
novel data-driven approach to analysing spontaneous, resting
brain activity in functional MRI, we found strong support for
both predictions. Indeed, the observed pattern of correlation differences was strikingly selective to a smaller subset of social brain
regions. The strongest decreases in autism spectrum disorders
were observed between limbic-related brain regions thought to
process affective aspects of social behaviour (Cluster 1) and the
remaining brain regions that are more involved in language/
communication-related (Cluster 2) and visuospatial/motor-related
(Cluster 3) aspects. Although a number of the brain regions found
to exhibit group differences in correlations are clearly involved in
non-social functions (e.g. occipital, parietal and somatosensory regions of interest in Cluster 3), these regions appear to have been
identified primarily through their lack of correlation in the autism
spectrum disorders group with the limbic regions of interest in
Cluster 1, all of which are more selectively engaged in social
task contexts (Mitchell, 2009; Simmons and Martin, 2009;
Fanselow and Dong, 2010). Combined with the relatively preserved levels of correlation among limbic regions in autism spectrum disorders, these results indicate a fractionation of the activity
dynamics between the limbic circuit and other regions of the social
brain, rather than a complete degeneration of limbic circuit activity. This same pattern of fractionation was independently obtained
for the autism spectrum disorders group when examining the
quantitative relationship between Region X Region correlation
values and scores on the Social Responsiveness Scale, a behavioural instrument specifically designed to measure impaired social
functioning on a quantitative scale (Constantino, 2002). A separate whole-brain correlation analysis with Social Responsiveness
Scale scores using the autism spectrum disorders connectedness
maps confirmed that the effects were driven by reduced overall
correlations of activity with Cluster 1 regions of interest. Aside
from replicating the overall pattern of Region X Region
interrelationships present in the group comparisons, the correlation
with Social Responsiveness Scale scores solely within the autism
spectrum disorders group strongly suggests that the group differences in correlation observed relative to the typically developing
participants reflect something about the social aspects of autism
spectrum disorders rather than some of the non-social characteristics that can accompany these disorders. The selective pattern of
correlation differences we observed is consistent with a decoupling
of brain regions that are more involved in affective aspects of
social processing from regions that mediate language/communication abilities and the perception of socially relevant form and
action. This pattern could not be anticipated by more general information processing views of impaired connectivity in autism
spectrum disorders that do not explicitly appeal to domain specific
social brain regions.
There are at least three distinct mechanisms that are capable of
explaining our pattern of results. The first, and perhaps the most
straightforward, is that there is a gross-disruption of anatomical
connections between limbic and non-limbic brain regions in autism
spectrum disorders. Previous post-mortem anatomical studies of
Brain 2012: 135; 2711–2725
| 2721
the autistic brain, although not always able to control for
co-morbid conditions such as epilepsy and intellectual disability,
have also highlighted abnormalities within limbic structures
(Kemper and Baumann, 1993; Schumann et al., 2004; Amaral
et al., 2008). In non-human primates, the amygdala and hippocampus and other brain regions that make up our Cluster 1 regions of interest (medial and ventromedial prefrontal cortex,
anterior aspects of the temporal lobes) are known to be monosynaptically connected and therefore form an anatomical circuit
(Carmichael and Price, 1995; Saleem et al., 2008). In typically
developing humans, these same limbic-related areas show distinct
growth trajectories across age relative to the rest of neocortex,
implying that distinct genetic mechanisms guide their development
(Shaw et al., 2008; Voineagu et al., 2011). Subsets of the regions
of interest in Clusters 2 and 3 are also likely to form distinct anatomical circuits, such as the language-related regions in Cluster 2
(Turken and Dronkers, 2011) and the occipital, occipitotemporal,
parietal regions in Cluster 3 (Felleman and Van Essen, 1991).
Activity coupling within the limbic circuit could be mediated
through preserved monosynaptic connections, whereas activity
decoupling with non-limbic neocortical circuits could result from
weak or lacking long-distance projections. A second possibility is
that the long-range anatomical connections are fully intact, but
that local structural or cellular abnormalities within the limbic circuit create aberrant local activity dynamics that undermine effective synchronization with more distal anatomical circuits that are
separated by longer conduction delays [see Yizhar et al. (2011) as
a recent example of this type of proposal]. This view makes clear
predictions for autism spectrum disorders in neuroimaging methods with high temporal and spatial resolution such as magnetoencephalography that the power spectrum of neural activity should
be altered locally in limbic-related brain regions, and this in turn
should lead to reduced coherence/phase-locking of activity with
non-limbic areas. A third possibility that we cannot entirely rule
out is that some explicit differences in mental state between participants with autism spectrum disorders and typically developing
participants during the rest scans explain our selective pattern of
correlation differences. There are several points to be made with
respect to this possibility: (i) to the extent that there are real brain
differences between these two groups, perhaps along the lines of
either of the first two mechanisms discussed above, then we
would certainly expect some differences in mental state; so our
main concern should be whether these are due to uninteresting
differences in mental state that are not central to the distinction
between autism spectrum disorders and control participants; (ii) if
the selective pattern of correlation differences observed here corresponds to a different mental state, it is a mental state that predicts the severity of social impairment in participants with autism
spectrum disorders, and therefore, it is clearly of interest to us; and
(iii) this pattern of correlation differences has not been reported
previously in resting-state or task-based functional connectivity
studies; if these results reflect differences in explicit thought,
they are providing novel information about the circuit-level functional organization of brain and mind that is relevant for the
understanding of autism spectrum disorders. All of that said,
there are a variety of reasons to discount this third possibility in
favour of the first two. The dynamics of resting blood oxygen
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| Brain 2012: 135; 2711–2725
level-dependent activity are concentrated at very low frequencies
(0.01–0.05 Hz) (Cordes et al., 2001), with single cycles of fluctuation taking up as much as 100 s. This would appear to be too
slow to correspond to the dynamic changes in explicit thought
that can occur on the timescale of a few seconds. Studies that
have attempted to evaluate the contribution of explicit thought
and/or mind-wandering to resting-state functional MRI correlation
patterns have observed some modest effects within the so-called
‘default’ network (Mason et al., 2007; Andrews-Hanna et al.,
2010a), but they are certainly not identical to the profile of
changes observed in this study, nor do they appear to be of
sufficient magnitude to explain the autism spectrum disorderstypically developing differences. Similar, spatially specific correlation patterns in spontaneous activity have also been observed
in monkeys under anaesthesia (Vincent et al., 2007; Margulies
et al., 2009) and in humans during sleep (Fransson et al., 2007;
Larson-Prior et al., 2009), conditions that eliminate the possibility
of explicit thought.
Limitations and relationship to
existing studies
Our study examined the extent to which functional connectivity
differences in high-functioning participants with an autism spectrum disorder without epilepsy or other co-morbid neurological/
genetic conditions are concentrated in regions of the social brain,
as well as the extent to which the severity of their social impairments are preferentially predicted by correlation levels among the
same social brain regions. Although our results may help to clarify
the neural bases of social impairments in this subgroup of participants with autism spectrum disorders, the degree to which our
results generalize to the larger autism spectrum disorders population is unclear. For example, participants with pronounced language delays were under-represented in this study due to our
weighting towards participants with Asperger’s syndrome. One
might expect a stronger concentration of correlation differences
in more classic language areas if this selection constraint were
relaxed. Similarly, if participants with lower levels of intellectual
functioning were included, then one might expect the results to
be more distributed among frontal brain regions that are associated with IQ test performance (such as lateral prefrontal and
anterior cingulate cortices; Duncan et al., 2000; Geake and
Hansen, 2005). Additional studies are needed in order to confirm
the applicability of our current results to these and other autism
spectrum disorders subgroups. We would speculate that if social
behaviours are indeed mediated by domain-specific neural
circuitry, then the results regarding the severity of autism spectrum
disorders social impairments should generalize to the autism spectrum disorders population at large, whereas the results in group
comparisons would hinge on the inclusion of appropriately
matched control groups. However, appropriate control groups
selected from the typically developing population may not exist
in some cases (e.g. with a general intellectual impairment and/or
severe language delay), perhaps requiring selection from other
clinical populations with relatively spared social and communication abilities.
S. J. Gotts et al.
It is also unclear to what extent the decoupling of limbic from
non-limbic social brain circuits that we observe is causally responsible for poor social functioning in autism spectrum disorders.
We cannot rule out the possibility that this decoupling resulted
indirectly from a long history of abnormal social functioning and
the under- or mis-utilization of the limbic circuit. In other words, it
is possible that this phenomenon is more of an experiencedependent effect rather than a cause. Age-stratified and/or
longitudinal developmental studies might help to clarify this
relationship, perhaps in conjunction with studies of the accompanying anatomical changes (Raznahan et al., 2010; Wallace
et al., 2010).
To our knowledge, only one other study to date has explored
functional connectivity differences in autism spectrum disorders
and typically developing populations in a whole-brain fashion
during rest. Anderson et al. (2011b) spatially sampled seed locations over an entire grey matter brain mask (7266 total seed regions of interest), and they used the results to classify subjects
either as having an autism spectrum disorder or as typically developing. They controlled for multiple comparisons using a mixture of
permutation tests and chance estimates derived from the binomial
distribution, allowing them to identify which brain regions contained the largest number of informative pairwise connections
for the autism spectrum disorders-typically developing classification. While their approach diverges from ours in a number of
respects, it nevertheless identified many of the same brain regions
(e.g. bilateral fusiform gyrus, medial prefrontal cortex, posterior
cingulate, temporo-parietal junction, anterior temporal cortex, anterior insula, superior parietal cortex and right orbitofrontal cortex;
compare with Supplementary Fig. 4). The effect size of group
differences present at individual Region X Region connections
has less of an influence on their estimate of which brain regions
are most relevant than it does in our approach (using group ttests), which may account for some of the quantitative differences
between our two studies. Their study was also more targeted
toward classification than toward detailed analyses of the
circuit-like interrelationships among the regions of interest, which
is the most novel contribution of our study. Another recent study
of naturally sleeping toddlers (1- to 3.5-years old), either autistic,
language-delayed or typically developing, utilized patterns of correlation across corresponding locations in the two cerebral hemispheres while the toddlers were passively exposed to auditory
stimuli such as words, pseudo-words, sentences, tones or environmental sounds (Dinstein et al., 2011). This approach does not
provide a whole-brain view of correlation differences per se [for
N voxels, N/2 interrelationships out of a possible N(N 1)/2],
but it does provide a much more systematic view of differences
throughout the brain than is afforded by a small handful of isolated seeds (see also Anderson et al., 2011a). Dinstein et al.
(2011) observed weaker correlations in the autistic toddlers relative to language delayed or typically developing toddlers between
homologous locations of the superior temporal gyrus/superior
temporal sulcus and the inferior frontal gyrus. Despite numerous
differences with our study (the age range and severity of subjects
with autism spectrum disorders, direct exposure to auditory stimuli, etc.), we also observed weaker correlations in the participants with autism spectrum disorders throughout the entire
Fractionation of social brain circuits
extent of the superior temporal gyrus/superior temporal sulcus
bilaterally, as well as the inferior frontal gyrus on the left
(Supplementary Fig. 4). The restriction of their findings to the
superior temporal gyrus/superior temporal sulcus and inferior frontal gyrus, which are involved in higher order auditory and linguistic
processing (for review see Turken and Dronkers, 2011), may have
resulted at least in part, from the use of overt auditory stimuli and
preserved stimulus-level variation around the mean response
(‘Discussion’ section) and/or from the focus on point-to-point
cross-hemispheric correlations.
More generally, the relationship between resting-state functional connectivity studies of autism spectrum disorders such as
ours and task-based connectivity studies that utilize similar participant selection criteria is unclear (for review, see Müller et al.,
2011). In task-based studies, only the condition means are
removed through regression, leaving the trial-level or stimuluslevel variation around the mean fully intact. Indeed, this variation
is what connectivity methods such as psycho-physiological interaction (Friston et al., 1997) and dynamic causal modelling (Friston
et al., 2003) rely on to show differences in connectivity between
task conditions within an experimental session. One might therefore expect results in studies that employ overt behavioural tasks
or the presentation of sensory stimuli to be biased towards stimulus/task-responsive brain regions. Consistent with this, Just et al.
(2004) employed a sentence comprehension task and found decreases in their autism group in both mean activity and interregional correlations in left hemisphere language areas (see also
Dinstein et al., 2011). Kleinhans et al. (2008) presented blocks
of face and house pictures to subjects with autism spectrum disorders and control subjects in a one-back task. Using a right fusiform face area seed, both subjects with autism spectrum disorders
and controls showed greater correlation with the right posterior
superior temporal sulcus and bilateral amygdala during the face
blocks relative to the house blocks, with controls showing significantly larger effects with the left amygdala and the posterior cingulate/precuneus. Mostofsky et al. (2009), using a sequential
finger-tapping task, found greater correlations in typically developing subjects relative to subjects with autism spectrum disorders
throughout the network of brain regions involved in motor execution (e.g. primary motor cortex, thalamus, supplementary motor
area and cerebellum). Studies of autism spectrum disorders that
have utilized inherently social or ‘theory of mind’ tasks have
tended instead to find results in multiple areas of the social
brain (Fig. 1) such as the medial prefrontal cortex, the posterior
superior temporal sulcus and temporo-parietal junction, the amygdala, the insula, and the lateral anterior temporal lobes (Castelli
et al., 2002; Lombardo et al., 2010; see also Di Martino et al.,
2009). Although we do not view this bias towards stimulus/
task-responsive brain regions as inherently problematic, it does
suggest that the choice of task/stimuli can have a direct impact
on the results and that results across different tasks/stimuli may
not be directly comparable. The virtue of the task-based approach
is that mental state is more explicitly controlled. In our view, an
integrated approach that combines resting-state and task-based
functional connectivity studies in the same subjects, utilizing a
wide range of social and non-social tasks with whole-brain analyses, would combine the strengths of both approaches. Such an
Brain 2012: 135; 2711–2725
| 2723
integrated approach might help to isolate the set of behavioural
circumstances that require the intact coordination of limbic and
non-limbic brain circuitry highlighted by our study.
Acknowledgements
The authors thank Lauren Kenworthy, John Strang and Rafael
Oliveras-Rentas for guidance and aid in autism spectrum disorder
(ASD) participant testing; Hang Joon Jo, Ziad Saad and Kelly
Barnes for helpful methodological advice; and members of the
Laboratory of Brain and Cognition, NIMH, for helpful feedback
and discussions.
Funding
This study was supported by the National Institute of Mental
Health, NIH, Division of Intramural Research.
Supplementary material
Supplementary material is available at Brain online.
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