Autism Spectrum Disorder Related Functional Connectivity Changes in the Language Network in Children, Adolescents and Adults

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disability with global implication. Altered brain connectivity in the language network has frequently been reported in ASD patients using task-based functional magnetic resonance imaging (fMRI) compared to typically developing (TD) participants. Most of these studies have focused on a specific age group or mixed age groups with ASD. In the current study, we investigated age-related changes in functional connectivity related measure, degree centrality (DC), in the language network across three age groups with ASD (113 children, 113 adolescents and 103 adults) using resting-state fMRI data collected from the autism brain imaging data exchange repository. We identified regions with significant group-wise differences between ASD and TD groups for three age cohorts using DC based on graph theory. We found that both children and adolescents with ASD showed decreased DC in Broca’s area compared to age-matched TD groups. Adults with ASD showed decreased DC in Wernicke’s area compared to TD adults. We also observed increased DC in the left inferior parietal lobule (IPL) and left middle temporal gyrus (MTG) for children with ASD compared to TD children and for adults with ASD compared to TD adults, respectively. Overall, functional differences occurred in key language processing regions such as the left inferior frontal gyrus (IFG) and superior temporal gyrus (STG) related to language production and comprehension across three age cohorts. We explored correlations between DC values of our findings with autism diagnostic observation schedule (ADOS) scores related to severity of ASD symptoms in the ASD group. We found that DC values of the left IFG demonstrated negative correlations with ADOS scores in children and adolescents with ASD. The left STG showed significant negative correlations with ADOS scores in adults with ASD. These results might shed light on the language network regions that should be further explored for prognosis, diagnosis, and monitoring of ASD in three age groups.

Full text

ORIGINAL RESEARCH
published: 18 August 2017
doi: 10.3389/fnhum.2017.00418
Autism Spectrum Disorder Related
Functional Connectivity Changes in
the Language Network in Children,
Adolescents and Adults
Yubu Lee1,Bo-yong Park 1,2,Oliver James 1,Seong-Gi Kim1,3 and Hyunjin Park1,4 *
1Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, South Korea, 2Department of
Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea, 3Department of Biomedical
Engineering, Sungkyunkwan University, Suwon, South Korea, 4School of Electronic and Electrical Engineering,
Sungkyunkwan University, Suwon, South Korea
Edited by:
Joshua Oon Soo Goh,
National Taiwan University, Taiwan
Reviewed by:
Hsiang-Yuan Lin,
National Taiwan University Hospital,
Taiwan
Chun-Hsien Hsu,
Institute of Linguistics, Academia
Sinica, Taiwan
*Correspondence:
Hyunjin Park
hyunjinp@skku.edu
Received: 23 March 2017
Accepted: 04 August 2017
Published: 18 August 2017
Citation:
Lee Y, Park B-y, James O, Kim S-G
and Park H (2017) Autism Spectrum
Disorder Related Functional
Connectivity Changes in the
Language Network in Children,
Adolescents and Adults.
Front. Hum. Neurosci. 11:418.
doi: 10.3389/fnhum.2017.00418
Autism spectrum disorder (ASD) is a neurodevelopmental disability with global
implication. Altered brain connectivity in the language network has frequently been
reported in ASD patients using task-based functional magnetic resonance imaging
(fMRI) compared to typically developing (TD) participants. Most of these studies have
focused on a specific age group or mixed age groups with ASD. In the current
study, we investigated age-related changes in functional connectivity related measure,
degree centrality (DC), in the language network across three age groups with ASD
(113 children, 113 adolescents and 103 adults) using resting-state fMRI data collected
from the autism brain imaging data exchange repository. We identified regions with
significant group-wise differences between ASD and TD groups for three age cohorts
using DC based on graph theory. We found that both children and adolescents with
ASD showed decreased DC in Broca’s area compared to age-matched TD groups.
Adults with ASD showed decreased DC in Wernicke’s area compared to TD adults.
We also observed increased DC in the left inferior parietal lobule (IPL) and left middle
temporal gyrus (MTG) for children with ASD compared to TD children and for adults
with ASD compared to TD adults, respectively. Overall, functional differences occurred
in key language processing regions such as the left inferior frontal gyrus (IFG) and
superior temporal gyrus (STG) related to language production and comprehension
across three age cohorts. We explored correlations between DC values of our findings
with autism diagnostic observation schedule (ADOS) scores related to severity of ASD
symptoms in the ASD group. We found that DC values of the left IFG demonstrated
negative correlations with ADOS scores in children and adolescents with ASD. The
left STG showed significant negative correlations with ADOS scores in adults with
ASD. These results might shed light on the language network regions that should
be further explored for prognosis, diagnosis, and monitoring of ASD in three age
groups.
Keywords: autism spectrum disorder, language network, resting-state fMRI, graph theoretical analysis,
age-related changes
Frontiers in Human Neuroscience | www.frontiersin.org 1August 2017 | Volume 11 | Article 418

Lee et al. Age-Related Connectivity in ASD
INTRODUCTION
Autism spectrum disorder (ASD) is a neurodevelopmental
disability characterized by impairments in language, social
interaction and restricted, repetitive, and stereotyped patterns of
behavior (American Psychiatric Association, 2000). ASD affects
2% of all children between the ages of 6 and 17 (Blumberg
et al., 2013). Deficits in use of language, semantic processing, and
interpreting language in context, are universal in ASD patients
(Howlin, 2003). Language ability is not only the earliest positive
prognostic indicator in children with ASD, but is also closely
related to long-term social function. Improved understanding of
the brain organization underlying language may shed light on
the neural basis of ASD and factors related to clinical severity
(Kleinhans et al., 2008). The identification of biomarkers in ASD
in language network would be helpful in ensuring an early and
accurate diagnosis as well as optimizing effective treatments.
Early task-based functional magnetic resonance imaging
(fMRI) studies reported functional connectivity differences of
cortical areas separately in children (Wang et al., 2006; Redcay
and Courchesne, 2008), adolescents (Knaus et al., 2008), adults
(Just et al., 2004; Kana et al., 2006; Gaffrey et al., 2007;
Mason et al., 2008; Tesink et al., 2009), or in two age groups
(Colich et al., 2012; Williams et al., 2013) on a range of
language tasks. Two studies in children with ASD reported
that hyper-connectivity in the right inferior frontal gyrus (IFG)
during irony processing (Wang et al., 2006) and in right and
medial frontal regions during speech perception relative to
typically developing (TD) children (Redcay and Courchesne,
2008). Wang et al. (2006) interpreted the hyper-connectivity as
the effortful use of normative neural circuitry associated with
the processing involved in understanding the mental states.
One study reported that adolescents with ASD also showed
hyper-connectivity in Broca’s area (left IFG) during semantic
integration and word generation task and adolescents with ASD
was less lateralized as compared with the TD adolescents (Knaus
et al., 2008). Adults with autism demonstrated pattern of relative
hyper-connectivity in Wernicke’s area (left superior temporal
gyrus (STG) and middle temporal gyrus (MTG)) compared
to Broca’s area in sentence comprehension task (Just et al.,
2004), semantic decision making task (Harris et al., 2006),
and word categorization task (Gaffrey et al., 2007). Two fMRI
studies in adults with autism revealed hyper-connectivity in
right temporal and right inferior frontal regions in response to
increased sentence difficulty or the presence of intentionality
(theory-of-mind) information during discourse comprehension
(Mason et al., 2008) and in right IFG for processing speaker
incongruent sentences (Tesink et al., 2009). The authors reported
that these results are consistent with a spillover account, in
which the right hemisphere homologs are recruited when the
left hemisphere language areas are taxed. One study reported
hypo-connectivity within the left hemisphere language network
during irony comprehension in children with ASD compared
with the TD children, while TD children showed activity in the
bilateral language network (Williams et al., 2013). Colich et al.
(2012) reported hypo-connectivity in Broca’s area as compared
with TD adolescents when viewing visual scenes and making
judgments about auditory ironic statements. Many existing fMRI
studies of language related areas in different age cohorts with
ASD have examined functional connectivity differences using
various language tasks. There has been agreement that functional
connectivity in each age cohort with ASD is different from TD,
but conflicting results have been reported in terms of which brain
regions are involved and whether they show hyper- or hypo-
connectivity.
Resting-state fMRI (rs-fMRI) has emerged as a powerful tool
for examining intrinsic functional brain connectivity without
selecting proper tasks for a broad range of ages and clinical
groups. The absence of task demands has enabled investigations
to examine increasingly earlier stages of development. That is,
it allows easier data collection from special populations such
as young children with ASD, who have difficulties with long
task-based fMRI experiments (Yerys et al., 2009). However,
research using task-independent rs-fMRI data to identify and
explore the language network in ASD is relatively scarce (Verly
et al., 2014). Verly et al. (2014) investigated the functional
connectivity of the language network including the cerebellum
in children with ASD using rs-fMRI. The study first performed a
verb-generation task using task-based fMRI to identify eight joint
language components using independent component analysis
(ICA), which were then used as seed regions for analysis of the
resting-state scan. This study indicated a significantly reduced
functional connectivity between IFG and left dorsolateral
prefrontal cortex as well as between IFG and right cerebellar
cortex children with ASD.
To date, most of these studies have focused on a single age
group (e.g., children, adolescents, or adults), mixed age groups,
or used a single group of participants spanning a large age range.
ASD patients show differential language capabilities as they age
from children to adults, and thus neuroimaging assessment of
ASD is better performed with these disparate age groups factored
in Padmanabhan et al. (2013). Resting-state fMRI studies of the
language network in ASD for identifying alterations in functional
connectivity in language related regions are scarce across three
age cohorts. Little is known about the developmental trajectories
of functional connectivity in the language network in ASD. To
address these developmental changes in language network using
rs-fMRI, functional connectivity needs to be examined in three
age cohorts with ASD.
Several fMRI studies found involvement of different networks
such as default mode (Mason et al., 2008; Tie et al., 2014; Verly
et al., 2014), frontoparietal (Kana et al., 2006; Verly et al., 2014;
Nair et al., 2015), and ventral attention (Mason et al., 2008; Shih
et al., 2010) networks for language processing. As suggested in the
recent literature, classical models of language processing did not
fully leverage recent developments in neuroimaging technology
and thus studies should consider expanded set of regions (or
networks) to better characterize language processing (Fox et al.,
2006; Lee et al., 2012; Tie et al., 2014; Verly et al., 2014; Nair
et al., 2015; Ardila et al., 2016). A recent study proposed that
there were two different language networks in the brain: first,
a language reception/understanding system, including a ‘‘core
Wernicke’s area’’ involved in word recognition (Brodmann Area
(BA) 21, BA 22, BA 41 and BA 42), and a fringe or peripheral
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Lee et al. Age-Related Connectivity in ASD
area (‘‘extended Wernicke’s area:’’ BA 20, BA 37, BA 38, BA
39 and BA 40) involved in language associations; second, a
language production system (‘‘Broca’s complex’’: BA 44, BA
45, and also BA 46, BA 47, parts of BA 6, and the thalamus)
from seven studies of fMRI activity during the performance of
different language activities (Ardila et al., 2016). Fox et al. (2006)
reported that Broca’s and Wernicke’s areas of language network
are, to a large extent, the left hemisphere homologs of the
right ventral frontal cortex and right temporo-parietal junction
(ventral attention network). Tie et al. (2014) reported that frontal
component indicated dominant activations in the left frontal and
temporal language areas (IFG, middle frontal gyrus (MFG), STG,
MTG, and precentral gyrus) and temporal component indicated
activations in more extensive regions (left temporal/parietal
cortex) for language network using rs-fMRI. Previous studies
also reported that language network was related to the ventral
attention network and was it had some spatial overlap with
ventral attention network which includes the right temporal-
parietal junction (supramarginal and superior temporal gyri)
and the right ventral frontal cortex (medial and inferior frontal
gyri). Thus, we considered many functional networks related to
language processing to assess connectivity differences consistent
with suggestion of the recent study (Tie et al., 2014; Verly et al.,
2014; Nair et al., 2015; Ardila et al., 2016).
In the present study, we investigated age-related changes in
functional connectivity related measure, degree centrality (DC),
in the language network across three age cohorts (children,
adolescents and adults) in ASD and TD groups using rs-fMRI.
In order to explore developmental changes in DC, we identified
group-wise differences between ASD and TD groups for three
age cohorts using DC, which provides information about a
node’s centrality or influence within the network based on graph
theory (Bullmore and Sporns, 2009). Centrality is a key concept
in network analysis and is well-suited to quantify connectivity
changes in rs-fMRI whether they are hypo or hyper-connective
(Buckner et al., 2009; Bullmore and Sporns, 2009; He et al.,
2009). We also investigated general trend of DC values with
respect to age cohorts for ASD and TD groups respectively.
Our aim was to explore developmental changes in DC for
children, adolescents, and adults in language network regions. In
addition, we explored if DC values of our findings were related to
autism diagnostic observation schedule (ADOS) scores in ASD
group. We performed post hoc correlation analysis between the
results of connectivity analysis and measure of clinical severity
such as ADOS communication and ADOS social interaction
scores. We hypothesized that DC in the language network using
rs-fMRI might demonstrate developmental changes across three
age groups with ASD. Particularly, we hypothesized that DC
deficit in key language processing regions might play a crucial
role for understanding developmental trajectories.
MATERIALS AND METHODS
Participants and Imaging Data
We acquired data from the Autism Brain Imaging Data
Exchange I (ABIDE I) and Autism Brain Imaging Data
Exchange II (ABIDE II), a publicly available dataset (Di Martino
et al., 2014). All data were obtained with informed consent,
in accordance with established human participant research
procedures. We performed the analyses on 676 participants
(329 ASD participants and 347 TD participants) recruited
from California Institute of Technology, University of Leuven
(Sample 1), New York University (Sample 1 and Sample 2),
University of Pittsburgh, University of California Los Angeles,
University of Utah, Yale Child Study Center, Georgetown
University, Katholieke Universiteit Leuven, Indiana University.
To explore the age-related differences in functional connectivity,
we divided the data into three age groups of ASD and TD
participants: children under 12 years of age (n= 240), adolescents
from 12 to 19 years of age (n= 231), and adults over 19 years of
age (n= 205). The ASD participants consisted of 113 children,
113 adolescents and 103 adults. The TD participants consisted
of 127 children, 118 adolescents and 102 adults. The ASD
participants had a clinical DSM-IV diagnosis of Autistic
Disorder, Asperger’s syndrome, or Pervasive Developmental
Disorder Not-Otherwise Specified (PDD-NOS) using ADOS
modules 3 or 4 (Lord et al., 1999) and the Autism Diagnostic
Interview–Revised (Lord et al., 1994). The ADOS was not
obtained for TD participants. Estimates of intelligence such
as FSIQ, VIQ and PIQ were measured using the Wechsler
Abbreviated Scale of Intelligence (WASI; Wechsler, 1999)
or the Wechsler Intelligence Scale for Children (WISC,
Wechsler, 1949). The ADOS has subscores for social interaction
(ADOS_SOCIAL) and communication (ADOS_COMM), which
are combined into a total score. As shown in Table 1, there were
no significant differences (p>0.05) in sex ratios, age, mean
framewise displacement (FD; Power et al., 2012), FSIQ, VIQ
and PIQ between ASD and TD groups within each of the three
age cohorts. Participant demographics and clinical information
are provided in Table 1. T1-weighted anatomical data and
functional data were acquired using a magnetization-prepared
gradient-echo (MPRAGE) sequence and using a gradient
echo, echo-planar imaging (EPI) sequence sensitive to blood
oxygenation level dependent (BOLD) contrast, respectively.
Detailed information on the anatomical and functional imaging
parameters used at each site are publicly available on the ABIDE
website1.
Image Preprocessing
We employed the AFNI (Cox, 1996) and FMRIB software
library (FSL, Jenkinson et al., 2012) for preprocessing of the
T1-weighted anatomical data. The skull tissue was removed
using 3dSkullStrip and the magnetic field bias was corrected
using FSL’s FAST tool. We processed the rs-fMRI data using
FSL software. The preprocessing steps included: (1) removal
of the first ten MRI volumes for adjusting the hemodynamic
response; (2) realigning data with 6 head motion parameters
using MCFLIRT; (3) identifying ‘‘bad’’ time points using a
threshold of FD >0.3 mm and the adjacent (i.e., one preceding
and two following) frames (Power et al., 2012); (4) slice timing
correction using SLICETIMER; (5) intensity normalization of the
1http://fcon_1000.projects.nitrc.org/indi/abide/
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Lee et al. Age-Related Connectivity in ASD
TABLE 1 | Demographic information for the ASD and TD participants in the three
age groups.
ASD TD p-value
Children (<12)
Gender (M : F) 100 : 13 110 : 17 0.66∗
Mean age (SD) 9.45 (1.63) 9.66 (1.42) 0.29
Age range 6.05–11.99 6.36–11.95
Mean FD (SD) 0.05 (0.02) 0.05 (0.02) 0.43
Full scale IQ (SD) 106 (14.70) 108 (10.77) 0.34
Verbal IQ (SD) 105 (13.90) 108 (11.43) 0.15
Performance IQ (SD) 107 (16.27) 106 (12.12) 0.94
ADOS communication 3.24 (1.51)
ADOS social 7.78 (2.48)
Adolescents (12–19)
Gender (M : F) 103 : 10 105 : 13 0.58∗
Mean age (SD) 15.02 (2.14) 14.68 (2.09) 0.23
Age range 12.03–19.64 12.01–19.8
Mean FD (SD) 0.04 (0.03) 0.04 (0.01) 0.80
Full scale IQ (SD) 106 (13.66) 108 (11.22) 0.21
Verbal IQ (SD) 106 (13.91) 108 (11.22) 0.22
Performance IQ (SD) 105 (14.55) 107 (12.36) 0.43
ADOS communication 3.46 (1.45)
ADOS social 7.92 (2.90)
Adults (>20)
Gender (M : F) 91 : 12 87 : 15 0.52∗
Mean age (SD) 26.03 (5.36) 25.47 (4.70) 0.43
Age range 20.0–39.2 20.0–39.39
Mean FD (SD) 0.04 (0.02) 0.03 (0.02) 0.10
Full scale IQ (SD) 110 (12.87) 112 (8.98) 0.12
Verbal IQ (SD) 110 (14.19) 113 (10.0) 0.12
Performance IQ (SD) 108 (13.62) 109 (9.38) 0.37
ADOS communication 3.58 (1.58)
ADOS social 7.26 (2.79)
∗Chi-squared test. ASD, autism spectrum disorder; TD, typical development;
M, males; F, females; SD, standard deviation; mean FD, mean framewise
displacement; IQ, intelligence quotient; ADOS, Autism Diagnostic Observation
Schedule.
fMRI time series data with a value of 10,000; (6) registration
of the functional images onto the preprocessed T1-weighted
anatomical images and subsequent registration to the Montreal
Neurological Institute (MNI) standard space; (7) regressing out
the nuisance variables including 24 motion related parameters
(Friston et al., 1996), FD of each time point, and signals extracted
from white matter and cerebrospinal fluid; and (8) temporal band
pass filtering of the voxel time series data retaining frequencies
from 0.009 to 0.08 Hz. Finally, we applied spatial smoothing with
a 6 mm full-width half-maximum (FWHM) Gaussian blur for all
analyses.
Region of Interest Definition
We defined 64 regions as regions of interest (ROIs) for the
functional connectivity analysis of the language network using
the Brainnetome Atlas with 246 subregions (210 cortical areas
and 36 subcortical regions) of the bilateral hemispheres2. The
ROIs were defined in the standard MNI space and thus they
could be applied to an individual participant’s neuroimaging data
which were registered onto the MNI space. The 64 functional
ROIs included the bilateral supplementary motor area (LSMA,
RSMA: BA 6), bilateral middle frontal gyri (LMFG, RMFG:
2http://atlas.brainnetome.org
BA 46), bilateral inferior frontal gyri (LIFG, RIFG: BA 44 and
BA 45), bilateral orbital gyri (LOrG, ROrG: BA 47), bilateral
superior temporal gyri (LSTG, RSTG: BA 22, BA 38, BA 41
and BA 42), bilateral middle temporal gyri (LMTG, RMTG:
BA 21 and BA 37), and bilateral inferior temporal gyri (LITG,
RITG: BA 20 and BA 37), and the bilateral inferior parietal
lobule (IPL, LIPL, RIPL: BA 39 and BA 40). Those ROIs are
involved in multiple functional networks related to language
processing including speech comprehension and production
and we designated the collection of ROIs as our language
network. Our definition of language network contained ROIs
of many functional networks and the regions were chosen to
explore language related differences. Existing studies also defined
language related regions using task fMRI and then explored
rs-fMRI properties in the identified regions. We used the same
approach except that the regions were derived from the existing
studies (Tie et al., 2014; Verly et al., 2014; Nair et al., 2015; Ardila
et al., 2016), not from our own task fMRI experiments.
Construction of the Language Network
Using Graph Theory
We constructed the language network using graph theory to
analyze functional connectivity among ROIs within the language
network. The network consists of a group of nodes connected
by edges in a graph structure (He et al., 2007; Bullmore and
Sporns, 2009). In our study, we adopted an undirected and
unweighted network model. Each ROI used for the language
network and the correlation value between two ROIs were
represented as node and edge, respectively. Edge values were used
as elements of the matrix, which is referred to as the correlation
matrix. We first calculated the Pearson correlation coefficient
between the mean signal intensity time courses of each ROI pair
for the correlation matrix of each participant. To reduce the
multisite effect from different data collection sites and potential
motion artifacts, we included dummy-coded site variables and
mean FD in the regression model for computing the Pearson
correlation values used to construct the correlation matrix. A
Fisher’s r-to-z transformation was applied to each correlation
matrix to obtain an approximately normal distribution of the
functional connectivity values. The transformed correlation
matrix was stacked. The correlation matrix was binarized by
applying a fixed threshold. The threshold was determined from
exploring a wide range of sparsity (1∼100%). We adopted
the minimum value at which all ROIs were connected in
the matrices of all participants, which was 19% (Seo et al.,
2013).
Connectivity Analysis
DC, betweenness centrality (BC), and eigenvector centrality
(EC) are widely used centrality measures to describe the brain
network and can be calculated for each brain region in order
to quantify the importance of a given node in terms of network
organization. DC is the number of edges connected to a node,
which quantifies the information passing through that particular
node. BC is the number of shortest paths between any two nodes
that run through that node, which represents the information
flow of a given node (Rubinov and Sporns, 2010). A node with
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Lee et al. Age-Related Connectivity in ASD
high DC or BC values is regarded as a hub node which plays
an important role in overall network organization (Xia et al.,
2014). EC is proportional to the sum of the EC of all nodes
directly connected to it. EC is able to capture an aspect of
centrality that extends to global features of a given network. DC
is one of the most common measures of centrality and it has a
straightforward neurobiological interpretation. The nodes with a
high DC values are interacting, structurally or functionally, with
many other nodes in the network (Rubinov and Sporns, 2010). It
is also reported to have higher test-retest reliability than other
nodal centrality metrics (Wang et al., 2011; Cao et al., 2014).
In our study, we employed DC as the main centrality measure
and also used both BC and EC for the additional analysis.
We identified regions with significant group-wise differences
between ASD and TD participants for all age cohorts (children,
adolescents and adults) in terms of DC values as well BC and EC
values.
Correlation of DC with ADOS Scores
We performed post hoc correlation analysis between our
connectivity findings and ADOS scores related to severity of
ASD symptoms in ASD group. Correlation analysis between
DC values of identified brain regions and two clinical scores
was explored to investigate whether the region with significant
differences in functional connectivity was related to the
symptoms of autism. DC values of each identified brain region
and two ADOS scores were correlated using a general linear
model. The significance of the correlation was quantified with
r- and p-value statistics. P-values were corrected using the
Holm-Bonferroni method (Holm, 1979).
Statistical Analysis
Statistical differences between ASD and TD groups within each
age cohort (children, adolescents, and adults) were assessed using
non-parametric permutation tests to avoid the issue of multiple
comparisons (Smith et al., 2013). ASD and TD participants
were randomly assigned to each age group 5000 times.
The null distribution of the DC values was computed from
random permutations. Statistically significant differences in DC
value were reported if the differences between ASD and TD
participants within each of three age groups did not belong to
the 95% of the null distribution (determined by two-tailed tests
with p<0.05, corrected). The quality of correlation between
DC values of each brain region and ADOS scores was quantified
using r- and p-value statistics. We applied the Holm-Bonferroni
method to obtain corrected p-values.
RESULTS
Participants with Head Motion
We excluded participants who had excessive head motion where
more than 40% of time points removed during the scrubbing
procedure. On this basis, we excluded 18 ASD participants
and six TD participants, leaving 329 ASD participants and
347 TD participants for the analysis. The number of participants
from each site that were included in our analysis are shown
in Supplementary Table S1. To rule out the possibility that
differences in head motion between each age cohort in ASD and
TD groups could contribute to the results, we calculated the mean
value of FD for each participant and compared it between the
two age groups. There were no significant differences (p>0.05)
in mean FD between each age cohort in ASD and TD groups
as shown in Table 1. We compared the distribution of mean
FD and percentage of bad time-points between ASD and TD
groups and also between each age cohort. The percentage of
bad time-points were 0.56% and 0.39% for ASD group and
for TD group, respectively. Percentage of bad time-points were
1.2%, 0.37% and 0.18% for children, adolescents, and adults with
ASD, respectively. Percentage of bad time-points is 0.72%, 0.35%
and 0.13% for TD children, TD adolescents, and TD adults,
respectively. The distributions of mean FD and percentage of bad
time-points for ASD and TD groups and for each age cohort are
plotted in Supplementary Figure S1.
DC Differences between ASD and TD
Groups
We identified regions with group-wise differences between ASD
and TD participants within each of three age groups using
DC values (Table 2). We visualized the regions with significant
(p<0.05, corrected) DC difference in red (ASD >TD) and
blue (TD >ASD) for each age group (Figures 1A,2A,3A).
DC value of each region for which there is a significant DC
difference was displayed in bar chart with error bar, which
is standard error of the mean (Figures 1B,2B,3B). Two
regions including the left IFG and left IPL exhibited significant
(p<0.05, corrected) differences between children with ASD
and TD children. Children with ASD showed decreased DC in
the left IFG and increased DC in the left IPL relative to TD
children (Figure 1B). The left IFG showed significant differences
between adolescents with ASD and TD adolescents. Adolescents
in ASD exhibited decreased DC in the left IFG compared to
TD adolescents (Figure 2B). Comparing between adults with
ASD and TD adults, two regions including the left STG and
MTG exhibited significant differences. Adults with ASD showed
decreased DC in the left STG and increased DC in the left
MTG compared to TD adults (Figure 3B). The 64 ROIs selected
for the centrality analysis of the language network are shown
in Supplementary Figure S2A and p-values of regions with
the significant group-wise differences between children with
ASD and TD children, between adolescents with ASD and TD
TABLE 2 | Regions with significant group-wise differences between ASD and TD
participants within three age groups using DC.
ASD vs. TD Significant MNI coordinates p-value,
regions corrected
x y z
Children LIFG −46 13 24 0.0094
LIPL −56 −49 38 0.0338
Adolescents LIFG −46 13 24 0.0140
Adults LSTG −50 −11 1 0.0204
LMTG −65 −30 −12 0.0376
L, left; IFG, inferior frontal gyrus; IPL, inferior parietal lobule; STG, superior temporal
gyrus; MTG, middle temporal gyrus.
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Lee et al. Age-Related Connectivity in ASD
FIGURE 1 | Degree centrality (DC) in regions with significant differences between children with autism spectrum disorder (ASD) and typically developing (TD) children.
(A) Regions with significant (p<0.05, corrected) difference are represented in red (ASD >TD) and blue (TD >ASD) for children. (B) Bar charts show DC values of
regions for which there are significant differences in children with ASD and TD children. The ASD group is displayed in red and the TD group in blue with error bar.
L, left; IFG, inferior frontal gyrus; IPL: inferior parietal lobule.
FIGURE 2 | DC in regions with significant differences between adolescents with ASD and TD adolescents. (A) Regions with significant (p<0.05, corrected)
difference are represented in red (ASD >TD) and blue (TD >ASD) for adolescents. (B) Bar charts show DC values of regions for which there are significant
differences in adolescents with ASD and TD adolescents. The ASD group is displayed in red and the TD group in blue with error bar. L, left; IFG, inferior frontal gyrus.
adolescents and between adults with ASD and TD adults are
shown in Supplementary Figures S2B–D, respectively.
BC and EC Differences between ASD and
TD Groups
Additional analysis results for identifying regions with
group-wise differences between ASD and TD participants
using both BC and EC were reported in the Supplementary
Material (Supplementary Tables S2, S3). Using BC, five regions
including the bilateral IFG, left orbital gyrus, right STG, and
right ITG exhibited significant differences between children with
ASD and TD children. The left MFG, right IFG, and right ITG
showed significant differences between adolescents with ASD
and TD adolescents. There were no regions with significant
differences between adults with ASD and TD adults. Using EC,
the right ITG showed significant differences between children
with ASD and TD children. The left MTG and right ITG showed
significant differences between adolescents with ASD and TD
adolescents. Comparing between adults with ASD and TD
adults, four regions including the left STG, left MTG, right ITG
and right IPL exhibited significant differences.
The identified regions using different centrality measures
were slightly different. No regions were commonly identified for
all three centrality measures. The main centrality measure in this
study was DC, so we compared what regions were commonly
identified using DC and BC (or DC and EC). The left IFG
commonly exhibited significant differences between children
with ASD and TD children for both DC and BC. The left STG and
MTG showed significant differences between adults with ASD
and TD adults for both DC and EC. As shown in Supplementary
Figure S3A, children with ASD showed decreased BC in the
left IFG relative to TD. Comparing adults with ASD and TD
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Lee et al. Age-Related Connectivity in ASD
FIGURE 3 | DC in regions with significant differences between adults with ASD and TD adults. (A) Regions with significant (p<0.05, corrected) difference are
represented in red (ASD >TD) and blue (TD >ASD) for adults. (B) Bar charts show DC values of regions for which there are significant differences in adults with ASD
and TD adults. The ASD group is displayed in red and the TD group in blue with error bar. L, left; STG, superior temporal gyrus; MTG, middle temporal gyrus.
FIGURE 4 | Correlation between autism diagnostic observation schedule (ADOS) scores and DC values of the identified regions for three age cohorts with ASD.
(A) Correlation between DC value of left inferior frontal gyrus (IFG) and ADOS_COMM score in children with ASD; (B) Correlation between DC value of left IFG and
ADOS_COMM score in adolescents with ASD; (C) Correlation between DC value of left superior temporal gyrus (STG) and ADOS_COMM score in adults with ASD.
adults, adults with ASD showed decreased EC in the left STG
and increased EC in the left MTG compared to TD adults as
shown Supplementary Figure S3B. The identified regions showed
consistent tendency of increasing or decreasing regardless of the
adopted centrality measures (DC, BC and EC) comparing age
matched ASD and TD groups.
Correlation of DC with ADOS Scores
We investigated the correlations between ADOS scores and DC
values of regions for which the DC was significantly different
(p<0.05, corrected) in ASD group. The left IFG demonstrated
significant negative correlations with ADOS_COMM score in
children with ASD (r=−0.4022, p= 0.0029; Figure 4A)
and in adolescents with ASD (r=−0.3510, p= 0.0035;
Figure 4B). Significant negative correlations were revealed
between ADOS_COMM score and the left STG in adults
with ASD (r= 0.3337, p= 0.0064; Figure 4C). Table 3
provides details of the correlation results. We also performed
the additional correlations between ADOS scores and BC and
EC values of regions for which the BC and EC was significantly
different (p<0.05, corrected) in ASD group (Supplementary
Tables S4, S5). Significant negative correlation was revealed
between ADOS_COMM scores and EC values of the left STG
in adults with ASD (r= 0.2751, p= 0.0465). We found
that there were no statistically significant correlations between
ADOS scores and BC values of regions for which the BC was
significantly different in three age cohorts with ASD.
DISCUSSION
In this study, we investigated age-related changes in DC,
functional connectivity related measure, in the language network
for three age cohorts in ASD and TD groups. We observed
decreased DC in the left IFG and increased DC in left IPL in
children with ASD compared to TD children. Decreased DC in
the left IFG was shown in adolescents with ASD relative to TD
adolescents. TD adolescents showed higher DC in left inferior
frontal regions compared to adolescents with ASD. Comparing
adults in the ASD and TD groups, we found decreased DC in
the left STG and increased DC in the left MTG in TD adults
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Lee et al. Age-Related Connectivity in ASD
TABLE 3 | Correlation between ADOS scores and DC values of the identified regions.
ASD ROIs ADOS_COMM ADOS_SOCIAL
r-value p-value, corrected r-value p-value, corrected
Children LIFG −0.4022 0.0029 −0.1948 0.1358
LIPL 0.0013 1 0.0045 1
Adolescents LIFG −0.3510 0.0035 −0.2218 0.0525
Adults LSTG 0.3337 0.0064 −0.1462 0.2075
LMTG 0.0474 1 −0.0351 0.7634
ADOS_COMM, autism diagnostic observation schedule communication score; ADOS_SOCIAL, autism diagnostic observation schedule social score. Significant regions
and statistical results (p <0.05, corrected) are in bold and italicized.
compared to adults with ASD. Children and adolescents with
ASD showed decreasing DC in left IFG compared to age-matched
TD groups. We also investigated correlations of ADOS scores
with DC values for identified regions. ADOS_COMM score
showed significant (p<0.05, corrected) negative correlations
with DC values in the left IFG in children and adolescents
with ASD. The left STG demonstrated significant negative
correlations with ADOS_COMM score in adults with ASD.
Language processing may be differentially affected in each age
group with ASD. To date, fMRI studies of language processing
in ASD have examined functional connectivity differences in
only one age group such as in children (Wang et al., 2006;
Redcay and Courchesne, 2008), adolescents (Knaus et al., 2008),
or adults (Just et al., 2004; Kana et al., 2006; Gaffrey et al., 2007;
Mason et al., 2008; Tesink et al., 2009), or in two age groups
(Colich et al., 2012; Williams et al., 2013) using different language
tasks. The existing findings were inconsistent with respect to
the regions involved in language processing. Both task-based
and resting-state fMRI have been applied to the study of
functional connectivity in ASD. It is clear that the methodological
choice in both task-based and resting-state approaches can
affect outcomes in autism neuroimaging studies (Müller et al.,
2011). Connectivity findings derived from rs-fMRI and task
fMRI could be divergent for language networks. Additionally,
the activation patterns revealed by task-based language fMRI
are highly variable across different language tasks (Tie et al.,
2014). Despite this issue of difficulty in comparisons between
task-based and rs-fMRI functional connectivity, we relate our
findings derived from rs-fMRI with existing fMRI findings
below.
One study reported that children with ASD had significantly
greater activation in the right IFG, left MTG, STG and left
postcentral gyrus as compared with TD children during irony
processing (Wang et al., 2006). Redcay and Courchesne reported
that children with ASD showed greater activation primarily
within right and medial frontal regions during speech perception
(Redcay and Courchesne, 2008). These studies in children
with ASD showed significantly increased connectivity in right
language areas. Williams et al. (2013) reported that the children
with ASD had lower activation within the left hemisphere
language network during irony comprehension as compared
with the TD children, while TD children showed activity in
the bilateral inferior frontal gyri. Lai et al. (2012) observed
decreased connectivity in left IFG in children with ASD relative
to TD children during speech stimulation. Our finding of
decreased DC in Broca’s area (left IFG) in children with
ASD compared to TD children is consistent with the previous
studies.
Comparing adolescents in the ASD and TD groups, our
results showed a decreased DC in Broca’s area in adolescents
with ASD. Knaus et al. (2008) reported increased activation
in the right IFG, MFG, MTG, precentral gyrus, orbito-frontal
gyrus, and superior parietal gyrus in adolescents with ASD
during a response naming task. Two studies reported decreased
connectivity in Broca’s area in adolescents with ASD during
semantic integration and word-generation task (Knaus et al.,
2008) and viewing visual scenes and making judgments about
auditory ironic statements (Colich et al., 2012). Our results
showed decreased DC in the left IFG compared to TD
adolescents. The previous studies are consistent with our study.
Taken together, we found significantly decreased DC in the left
IFG in both children and adolescents with ASD compared to
age-matched TD groups. Some authors reported that decreased
connectivity in left IFG during language tasks might be associated
with language specific deficits that could result from upstream
impairments in perceptual or linguistic processing of speech
stimuli in ASD (Müller, 2007; Groen et al., 2008).
Several studies in adults with ASD have demonstrated
differences in Wernicke’s area (left MTG and STG) during
semantic tasks, with some studies reporting increased activation
of Wernicke’s area in adults with ASD (Just et al., 2004; Harris
et al., 2006; Gaffrey et al., 2007). Wernicke’s area has traditionally
been defined as the posterior STG and accepted as playing a
pivotal role in word comprehension (Ross, 2010). Later, the left
posterior superior temporal sulcus (Price, 2000), MTG, angular
gyrus, and the supramarginal gyrus were also defined as parts of
Wernicke’s area (Bogen and Bogen, 1976; Rauschecker and Scott,
2009). Our finding showed that adults with ASD had decreased
DC in the left STG and increased DC in the left MTG compared
to TD adults. Our finding of increased DC in Wernicke’s area
(left MTG) in adults with ASD is partly consistent with the
previous studies.
In our study, we explored if our findings of altered DC
in the language network were related to ADOS scores, which
is a diagnostic tool that measures clinical severity of ASD. In
the current study, notable results were that the ADOS_COMM
score was negatively correlated with DC values in the left IFG
in children and adolescents with ASD. Also of interest was
that the ADOS_COMM score was negatively correlated with
the left STG in adults with ASD. Several rs-fMRI studies have
Frontiers in Human Neuroscience | www.frontiersin.org 8August 2017 | Volume 11 | Article 418

Lee et al. Age-Related Connectivity in ASD
found significant relationship between functional connectivity
measures and autism characteristics measured by the ADOS
scores (Monk et al., 2009; Assaf et al., 2010; Dinstein et al.,
2011; Nair et al., 2013; Redcay et al., 2013). Dinstein et al.
(2011) reported higher ADOS_COMM score with decreased
connectivity in the IFG. Similarly ADOS_COMM scores were
reported to be negatively correlated with the right lateral parietal
to anterior medial prefrontal cortex connectivity by Redcay
et al. (2013). ADOS_COMM and ADOS_SOCIAL scores were
also reported to be negatively correlated with right motor
cortex to thalamus connectivity (Nair et al., 2013) and with
connectivity z-scores of precuneus (Assaf et al., 2010). Monk et al.
(2009) reported that higher ADOS_SOCIAL score correlated
with decreased posterior cingulate cortex to right supplementary
motor area connectivity. They also reported that severe ASD
symptom was correlated with increased posterior cingulate
cortex to right posterior parahippocampal gyrus connectivity
(Monk et al., 2009). Many of the commonly reported regions with
decreased functional connectivity in ASD group are known to
be involved in the relevant behavioral capacities. The degree to
which these altered connectivities are predictive and specific for
the altered behaviors is unknown (Rane et al., 2015). In our study,
a significant negative relationship between ADOS_COMM score
and DC value in the two regions might account for the greater
impairment related to decreased DC in these regions. Our
results can therefore be considered reinforcing, as neuroimaging
analyses were closely linked with ADOS, a known indicator of
ASD severity.
Our study has some limitations. First, our definition of
language related ROIs came from many functional networks.
Thus, the chosen ROIs do reflect language processing regions
but also are related to other functions as well. A smaller set of
ROIs more specific to language processing would potentially lead
to more sensitive results. Second, our study is a cross-sectional
rather than a longitudinal study, and thus the conclusions that
can be drawn regarding the developmental process in ASD are
limited. To validate this issue, we need to study connectivity
differences in longitudinal data, which is left for future studies.
Still, the age-related changes of the regions identified in the
current study could be helpful for future studies tracking
developmental stages of functional connectivity in the language
network in ASD.
CONCLUSION
In summary, the current study investigated differences in DC
of language network between ASD and TD groups across
three age cohorts using rs-fMRI. We found that both children
and adolescents with ASD showed decreased DC in Broca’s
area compared to age-matched TD groups. Adults with ASD
showed decreased DC in Wernicke’s area (left STG), whereas
adults with ASD showed increased DC in the left MTG
compared to TD adults. We also observed increased DC in
the left IPL in children with ASD compared to TD children.
Furthermore, the DC values of left IFG in the language network
demonstrated negative correlations with ADOS scores related
to autism severity in children and adolescents with ASD.
The left STG demonstrated significant negative correlations
with ADOS score in adults with ASD. Overall, functional
differences occurred in key language processing regions such as
Broca’s area and Wernicke’s area related to language production
and comprehension across three age cohorts. We believe the
age-related changes of the regions identified in the current study
could be helpful for future studies considering developmental
stage of functional connectivity in the language network in
ASD.
ETHICS STATEMENT
The Institutional Review Board (IRB) of Sungkyunkwan
University approved our retrospective study. All subjects gave
written informed consent in accordance with the Declaration
of Helsinki. The protocol was approved by the IRB of
Sungkyunkwan University.
AUTHOR CONTRIBUTIONS
YL, S-GK and HP wrote the manuscript and B-yP and OJ aided
the experiments. HP is the guarantor of this work and, as such,
had full access to all the data in the study and takes responsibility
for the integrity of the data and the accuracy of the data analysis.
ACKNOWLEDGMENTS
This work was supported by the Institute for Basic Science
(IBS-R015-D1).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: http://journal.frontiersin.org/article/10.3389/fnhum.
2017.00418/full#supplementary-material
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
The reviewer H-YL and handling Editor declared their shared affiliation, and the
handling Editor states that the process nevertheless met the standards of a fair and
objective review.
Copyright © 2017 Lee, Park, James, Kim and Park. This is an open-access article
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Frontiers in Human Neuroscience | www.frontiersin.org 11 August 2017 | Volume 11 | Article 418