Title

Default mode network in young male adults with
autism spectrum disorder : relationship with
autism spectrum traits

Author(s)丁, ミンヨンCitation

大阪大学, 2015, 博士論文

Version Type VoR
URL

https://doi.org/10.18910/52077

rights
Note

Osaka University Knowledge Archive : OUKA
https://ir.library.osaka-u.ac.jp/
Osaka University

Default mode network in young male adults with autism spectrum disorder:
relationship with autism spectrum traits（ 青年期男性自閉症スペクトラム障害におけるデフォルトモードネットワークの特徴：
自閉症スペクトラム特性との関係性 ）大阪大学大学院
大阪大学・金沢大学・浜松医科大学・千葉大学・福井大学
連合小児発達学研究科
小児発達学専攻丁ミンヨン

２０１５年０１月 博士学位論文

Jung et al. Molecular Autism 2014, 5:35
http://www.molecularautism.com/content/5/1/35

RESEARCH

Open Access

Default mode network in young male adults with
autism spectrum disorder: relationship with autism
spectrum traits
Minyoung Jung1, Hirotaka Kosaka1,2,3,4*, Daisuke N Saito2,4, Makoto Ishitobi3,5, Tomoyo Morita6, Keisuke Inohara3,
Mizuki Asano2,3, Sumiyoshi Arai1, Toshio Munesue1,7, Akemi Tomoda1,2, Yuji Wada2,3, Norihiro Sadato4,8,Hidehiko Okazawa2,4 and Tetsuya Iidaka9

Abstract
Background: Autism spectrum traits are postulated to lie on a continuum that extends between individuals with
autism and individuals with typical development (TD). Social cognition properties that are deeply associated with
autism spectrum traits have been linked to functional connectivity between regions within the brain’s default mode
network (DMN). Previous studies have shown that the resting-state functional connectivities (rs-FCs) of DMN are low
and show negative correlation with the level of autism spectrum traits in individuals with autism spectrum disorder(ASD). However, it is unclear whether individual differences of autism spectrum traits are associated with thestrength of rs-FCs of DMN in participants including the general population.
Methods: Using the seed-based approach, we investigated the rs-FCs of DMN, particularly including the following
two core regions of DMN: the anterior medial prefrontal cortex (aMPFC) and posterior cingulate cortex (PCC) in 19
young male adults with high-functioning ASD (mean age = 25.3 ± 6.9 years; autism-spectrum quotient (AQ) = 33.4 ± 4.2;
full scale IQ (F-IQ) = 109.7 ± 12.4) compared with 21 age- and IQ-matched young male adults from the TD group(mean age = 24.8 ± 4.3 years; AQ = 18.6 ± 5.7; F-IQ = 109.5 ± 8.7). We also analyzed the correlation between the strengthof rs-FCs and autism spectrum traits measured using AQ score.
Results: The strengths of rs-FCs from core regions of DMN were significantly lower in ASD participants than TD
participants. Under multiple regression analysis, the strengths of rs-FCs in brain areas from aMPFC seed showed negative
correlation with AQ scores in ASD participants and TD participants.
Conclusions: Our findings suggest that the strength of rs-FCs in DMN is associated with autism spectrum traits in
the TD population as well as patients with ASD, supporting the continuum view. The rs-FCs of DMN may be usefulbiomarkers for the objective identification of autism spectrum traits, regardless of ASD diagnosis.
Keywords: Autism spectrum disorder (ASD), Autism spectrum traits, Autism-spectrum quotient (AQ), Default mode
network (DMN), Resting-state functional connectivities (rs-FCs), Anterior medial prefrontal cortex (aMPFC), Posterior
cingulate cortex (PCC)* Correspondence: hirotaka@u-fukui.ac.jp
1
Developmental Emotional Intelligence, Division of Developmental Higher
Brain Functions, Department of Child Development United Graduate Schoolof Child Development, Osaka University, Kanazawa University, Hamamatsu
University School of Medicine, Chiba University and University of Fukui,
Fukui, Eiheiji 910-1193, Japan
2
Research Center for Child Mental Development, University of Fukui, Fukui,
Eiheiji 910-1193, Japan
Full list of author information is available at the end of the article© 2014 Jung et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,
unless otherwise stated.

Jung et al. Molecular Autism 2014, 5:35
http://www.molecularautism.com/content/5/1/35

Background
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by impaired social
communication and social interaction, and unusually restricted, repetitive behaviors and interests [1,2]. These
characteristics are postulated to lie on a continuum that
extends between individuals with autism and individuals
with typical development (TD) [3,4]. This continuum
view suggests the possibility that autism spectrum traits
are found not only at high levels in individuals with
ASD but also at lower levels among individuals without
ASD [5-7]. This continuum view shifts us away frommerely categorical diagnosis towards the quantitative support of daily difficulties associated with autism spectrum
traits of each individual regardless of ASD diagnosis.
Taking the quantitative support into consideration necessitates the use of an instrument that can quantify autism
spectrum traits in the entire population; such an instrument could also be used to define the broader autism
phenotype [8,9].
To identify the level of autism spectrum traits, several
instruments have been developed. The autism-spectrum
quotient (AQ) questionnaire is a useful instrumentthat was developed for identifying the extent of autistic spectrum traits, assuming that adults of normal
intelligence may have autism spectrum traits at various levels [10]. Moreover, AQ is a validated measureof autism spectrum characteristics found within boththe typical population and individuals with a diagnosis ofautism [10,11].
Recent behavioral studies have shown that individual
differences in autism spectrum traits measured using
AQ are associated with the performance of social cognition processing tasks such as self-focused attention,
mind reading, episodic memory, and inferring others’
mental state in both a population with ASD and thatwith TD [12-15]. Neuroimaging studies also demonstratethat autism spectrum traits measured using AQ are associated with structural and functional abnormalities inbrain regions including the insula, inferior frontal gyrus,
and posterior cingulate cortex (PCC), which are involved
in social cognition processing in individuals with andwithout ASD [16-18]. Taken together, these findings indicate that it is very important to evaluate the neuroimaging studies considering the level of autism spectrum
traits.
Social and self-referential cognitive processes havebeen linked with cortical midline brain regions such as
PCC and the medial prefrontal cortex (MPFC), reflecting
the high functional connectivities within the default mode
network (DMN) [19,20]. In healthy individuals with TD,
the functional connectivities of DMN are consistently
deactivated when the individual is engaged in a cognitively
demanding, goal-oriented task [21]. On the other hand,

Page 2 of 11

brain regions of the DMN are often engaged during social
cognition processing tasks such as self-reference and the‘theory of mind’ [22-24]. This marked overlap between
brain regions of the DMN and regions of the ‘social brain’
suggests that the brain regions of the DMN are strongly
associated with the social cognition process [25,26].
Several studies of the DMN in individuals with ASD
have shown its lower functional connectivities in restingstate functional magnetic resonance imaging (rs-fMRI),
using the approaches of both region of interest (ROI) analysis and independent component analysis (ICA), than in
individuals with TD [27-32]. In addition, these studies also
showed negative correlations between the strength of theresting-state functional connectivity (rs-FC) of DMN andautism spectrum traits including social deficits in individuals with ASD. However, these studies have primarily investigated correlations between the strength of rs-FCs of
DMN and autism spectrum traits only in individuals withASD [27,29,31]. To the best of our knowledge, the individual differences in autism spectrum traits regarding social
cognition processing, which may also be associated withthe strength of rs-FCs of DMN in individuals with or without ASD, have not been evaluated.
The aims of this study were: (1) to clarify the rs-FCs of
DMN in high-functioning young male adults with ASD
by comparison with age- and IQ-matched young maleadults with TD on the basis of the location of ROIs
within DMN during rs-fMRI; and (2) to evaluate correlations between the strength of rs-FCs of DMN and autism
spectrum traits measured using AQ scores in TD participants and ASD participants, respectively.

Methods
Participants

Nineteen male individuals with high-functioning ASD
were recruited by the Department of Neuropsychiatryat the University of Fukui Hospital, Japan, and theDepartment of Psychiatry and Neurobiology of the
Kanazawa University Hospital, Japan. The authors (HKand TMu) diagnosed the participants on the basis of
the criteria in the Diagnostic and Statistical Manual of
Mental Disorders (DSM-IV-TR) [33] and standardized criteria taken from the Diagnostic Interview for Social andCommunication Disorders (DISCO) [34]. The DISCO isreported to have good psychometric properties [35]. It also
contains items on early development, and a section on activities of daily life, thereby giving the interviewer an ideaof the individual’s level of functioning in several different
areas, not only social functioning and communication[35]. All participants were right-handed [36]. Twenty-one
age-matched and intelligence quotient (IQ)-matched TD
male participants were recruited from the local community. Individuals with a history of major medical or neurological illness, including epilepsy, significant head trauma,

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or a lifetime history of alcohol or drug dependence, were
excluded from this study. They were screened to exclude
individuals who had a first degree relative with an Axis I
disorder, diagnosed on the basis of the DSM-IV criteria.
IQ was assessed using the Wechsler Adult Intelligence
Scale-III (WAIS-III) [37]. All the participants had full scale
IQ (F-IQ) scores >85. There were no significant differences between the groups in terms of age, verbal IQ, performance IQ, and F-IQ (all P >0.5). All the participantsalso completed the AQ questionnaire [10]. The protocol
used for this study was approved by the ethics committeeof the University of Fukui. After a complete explanation of
the study, all the participants provided written, informed
consent. Their mean age, handedness, IQ and AQ scoreare shown in Table 1.
fMRI data acquisition

Functional images were acquired with T2*-weighted
gradient-echo echo-planar imaging (EPI) sequence using
a 3-T imager (Discovery MR 750; General Electric Medical Systems, Milwaukee, WI, USA) and a 32-channnel
head coil. Two hundred and one volumes were acquiredin each participant. Each volume consisted of 40 slices,with a thickness of 3.5 mm and a 0.5 mm gap to cover
the entire brain. The time interval between two successive acquisitions of the same slice (repetition time, TR)
was 2,300 ms, with an echo time (TE) of 30 ms and a flip
Table 1 Demographic data, IQ and AQ scores of participants
MeasureASD participants

TD participants(n = 19)(n = 21)Age (SD)

25.3 (6.9)

24.8 (4.3)

Age (range)(16-40)(19-35)

Handednessa : Right / Left

19/0

21/0

F-IQ (SD)109.7 (12.4)

109.5 (8.7)

V-IQ (SD)

113.2 (15.7)

111.2 (10.6)

P-IQ (SD)103.9 (12.2)

105.3 (9.7)

33.4 (4.2)

18.6 (5.7)

Social skills

8.3 (1.5)

3.5 (2.3)

Communicationc

7.2 (1.5)

2.7 (2.1)

Attention switching

7.4 (1.8)

4.8 (1.5)

Imaginationc

5.8 (2.2)

3.9 (1.5)

Attention to detail4.7 (2.2)

3.7 (1.9)

angle (FA) of 81 degrees. The field of view (FOV) was
192 × 192 mm and the matrix size was 64 × 64, giving
volume dimensions of 3 × 3 mm. The participants were
instructed to close their eyes but stay awake and think ofnothing in particular. A total of 201 volumes were acquired for a total imaging time of 7 min 42 s. The experiment was conducted at the Biomedical Imaging ResearchCenter of the University of Fukui.
fMRI data analysis
Preprocessing

Data were analyzed using SPM8 software (Wellcome
Department of Imaging Neuroscience, London, UK).
After discarding the first five volumes, all volumes wererealigned spatially to the mean volume, and the signal
from each slice was realigned temporally to that obtained from the middle slice using sinc interpolation.
The resliced volumes were normalized to the Montreal
Neurological Institute (MNI) space with a voxel size of2 × 2 × 2 mm using the EPI template of SPM8. The normalized images were spatially smoothed with a 6-mm
Gaussian kernel.
Rs-fMRI datasets were processed using a toolkit of
the Data Processing Assistant for Resting-State fMRI
(DPARSF; http://www.restfmri.net) [38]. We conducted
additional processing as follows: (1) removing the linear trend in the time series; and (2) performing temporally bandpass filtering (0.01-0.08 Hz) to reduce theeffects of low-frequency drift and high-frequency noise[39,40]. To control the non-neural noise in the timeseries [41]; (3) several sources of spurious variance, thatis, six parameters from the rigid body correction ofhead motion, white matter signals, CSF signals, andglobal signals were removed from the data through linear regression [42].

b

IQAutism spectrum traits
Total AQc (SD)ccaAssessed by the Edinburgh handedness inventory [36]. All participants
were right-handed.
bIntelligence quotient was measured using the Wechsler Adult Intelligence
Scale-Third Edition in all the participants.
c
P <0.01, with the independent-samples t-test, comparison between participants
with ASD and TD.
AQ, autism spectrum quotient; ASD, autism spectrum disorder; F-IQ, full scale
IQ; P-IQ, performance IQ; TD, typical development; V-IQ, verbal IQ.

Head movement parameters

Rs-FCs of DMN are significantly affected by the headmotion of participants during fMRI scanning; that is,long-distance correlations are decreased by participant
motion, whereas many short-distance correlations are
increased [43-47]. To investigate the effect of headmotion and motion artifacts in rs-FCs, the root meansquare (RMS) of six movement parameters obtained in
the realignment process (x-, y-, z translations and x-, y-, zrotations), mean frame-to-frame RMS motion [43] andframe-wise displacement (FD) [45] were calculated foreach participant. There were no significant differences in
RMS (P values ranged from 0.17 to 0.70), mean frame-toframe RMS motion (P value was 0.11) and FD (P value
was 0.14) between the groups. In addition, there is no
significant relationship between AQ scores and the six
RMS head movement parameters (P values ranged from
0.10 to 0.78).

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Definition of ROIs

To clarify the rs-FCs of DMN in the present study, wedefined the regions in the anterior MPFC (aMPFC) and
PCC as ROIs. The ROI coordinates were selected fromthe DMN meta-analysis [48]. The seed regions of theaMPFC and PCC comprise core seeds within the functional connectivity of DMN, and their widespread connectivities are supported by connectional anatomy studies[48-50]. Two spherical ROIs of 8 mm radius centered at acoordinate listed in Table 2 were drawn for each participant in line.
Rs-FC analysis

After the processing of rs-fMRI data, we used the predefined seed regions for voxel-wise rs-FC analyses using
the DPARSF toolbox. The mean time course of all voxels
in each seed ROI was used to calculate voxel-wise linear
correlations (Pearson’s correlation) in the whole brain
during the rs-fMRI period. Individuals’ r values werenormalized to z values using Fisher’s z transformation.
At the group level, each image pertaining to z values
reflecting a correlation between the seed ROI and eachvoxel of the brain was entered into a random effecttwo-sample t-test to identify the whole-brain regions
showing significant differences between the groups
with ASD and TD. In seed-based rs-FC analyses, global time series, computed across all brain voxels, alongwith six motion parameters, were used as additional
covariates to remove confounding effects of physiologic noise and participant movement. For the presentation purpose, rs-FCs from the aMPFC and PCC seed
ROIs are shown separately for each group at the thresholdof P = 0.05 with a family wise error correction (Figure 1).
Results were corrected between group comparisons, set
at P = 0.001, uncorrected at peak level, and P = 0.05 withcluster level.
Relationships among rs-FCs of group differences with
AQ or IQ

To confirm the relationship between the significant difference in strength of rs-FCs and autism spectrum traits

or IQ, we calculated correlations between AQ scores,
F-IQ and the Z values, which were extracted from the regions showing the strength of rs-FCs of significant groupdifferences (Spearman’s rank order correlation coefficients;
the statistical threshold was set at P = 0.01).
Multiple regression analysis among the strength of rs-FC
with AQ, IQ, or age

To investigate the relationships among the strength of
rs-FC, autism spectrum traits, IQ, or age across eachgroup, we performed additional analyses of nine independent variables in a multiple regression analysis in
SPM8, AQ total scores, F-IQ, age, and RMS of six movement parameters as covariates. The statistical thresholdfor contrasts was P <0.001 uncorrected for height andcluster P <0.05 corrected for multiple comparisons. In
addition, we tested whether the five AQ subscale scores
would also show correlations with the brain regions andwe observed correlations in each group.

Results
Differences in the strength of rs-FCs in DMN between
groupsRs-FCs from the aMPFC and PCC seed regions were
distributed in the medial parts of the brain (Figure 1). In
the ASD group compared with the TD group, substantiallysmaller areas were functionally connected with these seedregions.
In the ROIs within the DMN, significant differences inthe strength of rs-FCs from the aMPFC seed were observed in the paracentral lobuli and middle frontal gyrus(MFG) between the two groups (Table 2, Figure 2). A
significant difference in rs-FCs from the PCC seed wasobserved in the MPFC between the groups (Table 2,
Figure 3). The mean correlation coefficient (Fisher ztransformed) for each group is shown in Figure 2 andFigure 3. The strengths of all of these rs-FCs were lower
in the ASD group compared with the TD group. Individuals with ASD showed no significantly higher strengthof rs-FCs from the seed regions compared with the TD
group.

Table 2 Significant differences in rs-FCs between groups with ASD and TD (TD >ASD)
Seed region

Region

MNI coordinatesx

aMPFC
Paracentral lobule

Z-score
y

z

-6

52

-2

4

-30

72

Cluster sizekE (voxels)

4.21

138

Paracentral lobule

-10

-36

74

4.11

176

Middle frontal gyrus

-24

25

48

4.51

89

-8

-56

26

-6

54

-12

3.92

82

PCC
Medial prefrontal cortex

The statistical threshold for contrasts was P <0.001 uncorrected for height and cluster P <0.05 corrected for multiple comparisons.
aMPFC, anterior medial prefrontal cortex; MNI, Montreal Neurological Institute; PCC, posterior cingulate cortex.

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Figure 1 Results of rs-FCs from the aMPFC or PCC seed for each group. (A) Rs-FCs from the aMPFC seed ROI are shown for each group.
(B) Rs-FCs from the PCC seed ROI are shown for each group. The statistical threshold for both results was set at P = 0.05 with FWE correction andk = 10 voxels for presentation purpose. The region with functional connectivities was smaller in the ASD group than in the TD group. The coordinates(A) (-3, -60, 32); (B) (-4, -65, 24) at the figure) were selected from a previous study [52]. Color bar denotes t-statistic range.

Relationship between the strength of rs-FCs of groupdifferences and AQ or IQ

In the ASD group, there were no significant correlations
between either the strength of rs-FCs and F-IQ or AQ
total score (Figure 2; Figure 3, P >0.01). In the TD group,
there were also no significant correlations between either the strength of rs-FCs and F-IQ or AQ total score(Figure 2; Figure 3, P >0.01).
Relationship with AQ or IQ in the multiple regression
analysis

In the ASD group, AQ total scores were significantly negatively correlated with the strength of rs-FC in aMPFC with
MFG and cerebellum (Table 3; Figure 4, P <0.01). We also
found that two AQ subscale scores (communication scores
and attention switching scores) also significantly negatively
correlated with the strength of rs-FCs in aMPFC with
MFG and cerebellum (P <0.05). In the TD group, AQ total
scores were significantly negatively correlated with thestrength of rs-FC in aMPFC with superior temporal gyrus(STG) and MTG (Table 3; Figure 4, P <0.01). We also
found that three AQ subscale scores (social skill scores,
communication scores, and attention switching scores)
also significantly negatively correlated with the strength of
rs-FCs in aMPFC with STG and MTG (P <0.01).
There were no significant correlations between thestrength of rs-FCs and each participant’s F-IQ in all theparticipants, in the ASD group or in the TD group, inthe multiple regression analysis (P >0.01).
Relationship with age in the multiple regression analysis

In the TD group, age was significantly positively correlated with the strength of rs-FCs in the aMPFC with
MTG and PCC with cingulate gyrus (Table 4; Figure 5).
We found no significant correlations between age andthe strength of rs-FCs of DMN, in the ASD group or all
the participants.

Discussion
We found that young male adults with high-functioning
ASD display lower rs-FCs between two seeds (aMPFC
and PCC) of DMN and other brain regions In addition,
we found that autism spectrum traits measured using
the AQ score are associated with the strength of rs-FCs
of DMN that contain brain regions relevant to social
cognition processing, in each TD participants and ASD
participants, respectively. These results complement previous studies [26,29,31], suggesting that the strengths of
rs-FCs of DMN are associated with autistic spectrum
traits of social cognition processing in ASD participants.
In addition, our results showed that this association between the strength of rs-FCs of DMN and autistic
spectrum traits also exists not only in ASD participants
but also in TD participants. These findings suggest that

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Figure 2 Results of rs-FCs from the aMPFC seed ROI between groups. (A) Comparison of rs-FCs from the aMPFC seed ROI between groups.
There were significant clusters in the paracentral lobuli (left) and middle frontal gyrus (right). Detailed information on these clusters is shown in
Table 2. (B) Mean and one standard error of Fisher’s z-transformed correlation coefficients extracted from the left paracentral lobule (left), rightparacentral lobule (middle), and middle frontal gyrus (right) clusters are shown for each group. Rs-FCs were lower in the ASD group than in the
TD group. (C) Scatter plots showing the correlations of the strength of rs-FCs in the aMPFC with left paracentral lobule (left), right paracentral
lobule (middle), and MFG (right) with AQ total scores in each group. (D) Scatter plots showing the correlations of the strength of rs-FC in the
aMPFC with left paracentral lobule (left), right paracentral lobule (middle), and MFG (right) with full-scale IQ score in each group. The red bar andsquares represent individuals with ASD, and the blue bar and circles represent participants with TD. Color bar denotes t-statistic range.

the strength of rs-FCs of DMN may underlie some of
the autism spectrum traits, regardless of ASD diagnosis.
Difference in functional connectivities of DMN between
ASD and TD groups

Our results mostly agree with those of other studies of
rs-FCs of DMN [27-32] that demonstrated lower rs-FCs
of DMN in ASD. However, as for MPFC, the regions
showing lower functional connectivities in previous studies
are not always in agreement with those found in this study[27-30,32]. Differences in approach (seed-based or ICA)
might have made direct comparisons between results
somewhat challenging [51]. Andrews-Hanna et al. [49]demonstrated that the aMPFC of DMN is associatedwith social cognition processes including judgments orremembering trait adjectives about themselves comparedwith other people, whereas the dorsal medial prefrontal
cortex (dMPFC) of DMN is associated with social cognition processes including self-referential judgments about
their present situation or mental states. These findings
taken together with our findings suggest that rs-FCs inbrain regions including both aMPFC and dMPFC areassociated with autism spectrum traits associated withprocesses of basic social cognition of self and others inindividuals with ASD.
DMN associated with autism spectrum traits

In recent studies of rs-FCs of DMN in ASD, correlations
have been investigated between autism spectrum traits
and functional connectivities within brain areas such as

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Figure 3 Results of rs-FCs from the PCC seed ROI between groups. (A) Comparison of rs-FCs from the PCC seed ROI between groups. There
was a significant difference in the medial prefrontal cortex. Detailed information on the cluster is shown in Table 2. (B) Mean and one standard
error of Fisher’s z-transformed correlation coefficients extracted from the medial prefrontal cortex are shown for each group. Rs-FCs were lower inthe ASD group than in the TD group. (C) Scatter plots showing the correlations of the strength of rs-FC in the PCC with MPFC with AQ total
scores in each group. (D) Scatter plots showing the correlations of the strength of rs-FCs in the PCC with MPFC with full-scale IQ score in eachgroup. The red bar and squares represent individuals with ASD, and the blue bar and circles represent participants with TD.

the precuneus [27], MPFC [27,29,32], anterior cingulate
cortex [27], and superior frontal gyrus [31]. However,most of these studies analyzed these correlations in only
ASD participants. In general, these studies suggest negative correlation between the strength of rs-FCs of DMN
and autism spectrum traits in individuals with ASD.
In the current findings, the strength of rs-FCs in brain
areas from aMPFC seed showed negative correlations
with AQ total score not only in ASD participants but alsoin TD participants in the multiple regression analysis,

although these areas from aMPFC seed that showednegative correlations with AQ total scores in two groups
were different from each other (Table 3). Moreover, among
AQ subscale scores, communication scores and attention
switching scores were related to the strength of rs-FCs of
DMN in the ASD group. On the other hand, social skill
scores, communication scores, and attention switchingscores were related to the strength of rs-FCs of DMN in
the TD group. Our findings suggest that the strength of rsFCs of DMN might underlie the level of autism spectrum

Table 3 Brain regions showing negative correlations between AQ and the strength of rs-FCs in multiple regression
analysisSeed region

Region

MNI coordinates

Z-scoreCluster size

x

y

z

kE (voxels)

Middle frontal gyrus

-46

26

46

4.28

101

Cerebellum

18

-82

-44

4.32

86

Superior temporal gyrus

-58

-40

4

4.12

112

Middle temporal gyrus

68

-14

-12

4.59

106

ASD participants
aMPFC

PCC

None

TD participants
aMPFC

PCC

None

The statistical threshold for contrasts was P <0.001 uncorrected for height and cluster P <0.05 corrected for multiple comparisons.
aMPFC, anterior medial prefrontal cortex; MNI, Montreal Neurological Institute; PCC, posterior cingulate cortex.

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Figure 4 Brain regions showing negative correlations between AQ and the strength of rs-FCs from aMPFC seed in each group. Brainregions showing negative correlations between AQ and strength of rs-FCs from aMPFC seed in multiple regression analysis. The statistical thresholdfor contrasts was P <0.001 uncorrected for height and cluster P <0.05 corrected for multiple comparisons. The scatterplots show the association of
AQ and the strength of rs- FCs in selected regions. aMPFC, anterior medial prefrontal cortex; MFG, middle frontal gyrus; MTG, middle temporalgyrus; STG, superior temporal gyrus; rs-FC, resting state functional connectivity.

traits in participants without ASD diagnosis or with subthreshold autism spectrum traits, supporting the continuum view. In addition, considering the results of themultiple regression analysis using AQ subscale scores, thenature of autism spectrum traits, which affects the strengthof rs-FCs of DMN in aMPFC seed, might be different ineach group and the function of DMN might not always be
the same between ASD and TD groups.
Age-related changes in functional connectivities of DMN

Particularly interesting from our viewpoint is the lack ofconsensus regarding the strength of rs-FC aspects of age

variation with ASD. In relation to age-related changes in
DMN in ASD, few studies have examined age correlatesof functional connectivities by rs-fMRI. To the best of
our knowledge, only one rs-fMRI study has examined
the functional connectivities in children with ASD compared with age- and IQ-matched children with TD [52].
The previous study showed higher rs-FCs of DMN in
PCC seeds in children with ASD than in children withTD [52], but we found that lower rs-FCs of DMN in PCC
seeds in the ASD group and a relationship between rs-FCs
of DMN in PCC seeds and age in the TD group. We
speculate that the age-related changes in the functional

Table 4 Brain regions showing correlations between age and the strength of rs-FCs in multiple regression analysis
Seed region

Region

MNI coordinatesxZ-scoreyz

Cluster sizekE (voxels)

All participants (Positive and Negative correlations)NONE
ASD participants (Positive and Negative correlations)
NONE
TD participants (Positive correlation)
aMPFC

Middle temporal gyrus

-62

-50

-6

4.26

71

PCC

Cingulate gyrus

2

-46

42

3.91

90

TD participants (Negative correlation)
NONE
The statistical threshold for contrasts was P <0.001 uncorrected for height and cluster P <0.05 corrected for multiple comparisons.
aMPFC, anterior medial prefrontal cortex; MNI, Montreal Neurological Institute; PCC, posterior cingulate cortex.

Jung et al. Molecular Autism 2014, 5:35
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Page 9 of 11

Figure 5 Brain regions showing positive correlations between age and the strength of rs-FCs from seed regions in TD participants.
Brain regions showing positive correlations between age and the strength of rs-FCs from seed regions in multiple regression analysis. The statisticalthreshold for contrasts was P <0.001 uncorrected for height and cluster P <0.05 corrected for multiple comparisons. The scatterplots show the associationof age and the strength of rs-FCs in selected regions. aMPFC, anterior medial prefrontal cortex; CingG, cingulate gyrus; MTG, middle temporal gyrus; PCC,
posterior cingulate cortex; rs-FC, resting state functional connectivity.

connectivities of DMN in ASD and TD may be linked todifferences in neurodevelopmental mechanisms in childhood and numerous variables including point of development. Future studies are required to explore the variationof rs-FCs of DMN with age for individuals with ASD.
Future directionsFirst, the size of each group in this study may be relatively too small to demonstrate a relationship between
the various levels of autism spectrum traits and thestrength of rs-FCs of DMN. Second, we defined the seedregions for rs-FC analyses on the basis of the location of
ROIs within the DMN in accordance with a previous
study. Although the seed-based analysis in the present
research makes it possible to determine rs-FC precisely,
this analysis did not reveal information about intrinsically connected networks and their interactions [51]. Future study is necessary to replicate these findings, using
the ICA approach for exploring information about intrinsically connected networks. Third, we included global
signal regression in generating the FC map. However, it isa quite controversial issue whether global signal regression

changes resting-state correlations and produces negative
correlations [53]. Thus, the findings used in the present
study are not definitive, and future study is necessary tocompare the results with confound regression strategies.
Finally, our participants were only young male adults withhigh-functioning ASD. Future study is needed to clarify rsFCs in both males and females, children with ASD, and
ASD individuals without high-functioning. These additional researches will help provide more complete picturesthat may clarify the etiology of ASD.

Conclusions
Young male adults with high-functioning ASD showed
lower rs-FCs of DMN compared with age- and IQmatched young male adults with TD. Moreover, thestrength of rs-FCs of DMN was associated with autism
spectrum traits in each ASD and TD group, regardlessof ASD diagnosis. We propose that the strength of rs-FCs
of DMN might underlie the level of autism spectrum traits
and might be one of the potential biomarkers for the objective identification of the level of autism spectrum traits,
regardless of ASD diagnosis.

Jung et al. Molecular Autism 2014, 5:35
http://www.molecularautism.com/content/5/1/35

Abbreviations
AMPFC: Anterior medial prefrontal cortex; AQ: Autism-spectrum quotient;
ASD: Autism spectrum disorder; DMN: Default mode network; DMPFC: Dorsal
medial prefrontal cortex; IQ: Intelligence quotient; PCC: Posterior cingulate
cortex; Rs-FC: Resting-state functional connectivities; TD: Typical development.

Page 10 of 116.

7.
Competing interests
The authors declare that they have no competing interests.
8.
Authors’ contributions
MJ was involved in conducting the experiment, analyzing and interpreting
data, and drafting the article. HK was involved in recruiting the participants,
diagnosing the participants with ASD, conducting the experiment, analyzing
and interpreting data, and drafting the article. DNS, TM (fifth author), and KI
were involved in recruiting the participants and conducting the experiment.
MI was involved in recruiting the participants, interpreting data, and drafting
the article. TM (eighth author) was involved in recruiting the participants anddiagnosing the participants with ASD. MA, SA, AT, YW, NS, and HO wereinvolved in interpreting the data. TI was involved in designing, analyzing
and interpreting data, and drafting the article. All the authors have readand approved the final manuscript.

9.
10.

11.

12.
Acknowledgments
We would like to express our sincere appreciation to the participants andtheir families, who generously and courageously participated in this research.
This research was funded in part by Grants-in-Aid for Scientific Research fromthe Japan Society for the Promotion of Science (21220005, 21591509, 21791120,
25293248), the Japan Research Foundation for Clinical Pharmacology, and the
Takeda Science Foundation. Part of this research was the result of ‘Developmentof biomarker candidates for social behavior’ and ‘Integrated research onneuropsychiatric disorders’ performed under the Strategic Research Programfor Brain Sciences by the MEXT of Japan.
Author details
1
Developmental Emotional Intelligence, Division of Developmental Higher
Brain Functions, Department of Child Development United Graduate Schoolof Child Development, Osaka University, Kanazawa University, Hamamatsu
University School of Medicine, Chiba University and University of Fukui,
Fukui, Eiheiji 910-1193, Japan. 2Research Center for Child Mental
Development, University of Fukui, Fukui, Eiheiji 910-1193, Japan. 3Departmentof Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Fukui,
Eiheiji 910-1193, Japan. 4Biomedical Imaging Research Center, University of
Fukui, Fukui, Eiheiji 910-1193, Japan. 5Department of Child and Adolescent
Mental Health, National Institute of Mental Health, National Center of
Neurology and Psychiatry, Kodaira, Tokyo 187-8553, Japan. 6Graduate Schoolof Engineering, Osaka University, Suita, Osaka 565-0871, Japan. 7ResearchCenter for Child Mental Development, Kanazawa University, Kanazawa,
Ishikawa 920-8641, Japan. 8Department of Cerebral Research, National
Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan.
9Department of Psychiatry, Graduate School of Medicine, Nagoya University,
Nagoya, Aichi 466-8550, Japan.
Received: 4 February 2014 Accepted: 27 May 2014
Published: 11 June 2014

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doi:10.1186/2040-2392-5-35
Cite this article as: Jung et al.: Default mode network in young maleadults with autism spectrum disorder: relationship with autism spectrum
traits. Molecular Autism 2014 5:35.

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