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ORIGINAL RESEARCH
Only-child and non-only-child exhibit differences in creativity
and agreeableness: evidence from behavioral and anatomical
structural studies
Junyi Yang
1,2,3
&Xin Hou
1,2,3
&Dongtao Wei
1,2,3
&Kangcheng Wang
1,2,3
&Ya d a n L i
1,2,3
&
Jiang Qiu
1,2,3
#Springer Science+Business Media New York 2016
Abstract Different family composition and size inevitably
make only-children different from non-only-children.
Previous studies have focused on the differences in behaviors,
such as cognitive function and personality traits, between the
only-child and the non-only-child. However, there are few
studies that have focused on the topic of whether different
family environments influence children’s brain structural de-
velopment and whether behavior differentially has its neural
basis between only-child and non-only-child status. Thus, in
the present study, we investigated the differences in cognition
(e.g., intelligence and creativity) and personality and the ana-
tomical structural differences of gray matter volume (GMV)
using voxel-based morphometry (VBM) between only-
children and non-only-children. The behavioral results re-
vealed that only-children exhibited higher flexibility scores
(a dimension of creativity) and lower agreeableness scores (a
dimension of personality traits) than non-only-children. Most
importantly, the GMV results revealed that there were signif-
icant differences in the GMV between only-children and non-
only-children that occurred mainly in the brain regions of the
supramarginal gyrus, which was positively correlated with
flexibility scores; the medial prefrontal cortex (mPFC), which
was positively correlated with agreeableness scores; and the
parahippocampal gyrus. These findings may suggest that fam-
ily environment (i.e., only-child vs. non-only-child), may play
important roles in the development of the behavior and brain
structure of individuals.
Keywords Only-child .Supramarginal gyrus .Medial
prefrontal cortex (mPFC) .Creativity .Agreeableness
Introduction
To ease the enormous pressure of population explosion, the
Chinese government started promoting and implementing an
only-child family planning program in 1979 (Jiao et al. 1996;
S. Li et al. 2013). The implementation of the one-child policy
lasted more than 30 years; the controlling effect of the popu-
lation growth is significant (Festini and de Martino 2004).
Today, there are manyonly children in the younger generation
of Chinese due to this policy. An only-child is defined as a
child who never had any siblings (Burke1956; Cai et al. 2012;
Fletcher 2014). Different family compositions and sizes may
determine the different modes of interaction between family
members; therefore, it is generally believed that the family
environment inevitably makes only-child groups different
from non-only-child groups in terms of the children’scogni-
tion, personality and affect characteristics (Feng 1992;Hao
and Feng 2002; S. Li et al. 2013). For example, previous
studies have revealed that only-children exhibit more positive
developmental outcomes (such as achievement, intelligence
and creativity) (Falbo et al. 1989; Gaynor and Runco 1992),
more positive relationships with their parents (Blake 1981;
Falbo and Polit 1986)andfewerbehavioralproblemsin
school (Falbo and Polit 1986;Fenton1928)comparedwith
non-only-children. Otherwise, only-children receive too much
Junyi Yang and Xin Hou contributed equally to this work.
*Jiang Qiu
qiuj318@swu.edu.cn
1
Key Laboratory of Cognition and Personality (SWU), Ministry of
Education, Chongqing 400715, China
2
Department of Psychology, Southwest University,
Chongqing 400715, China
3
Southwest University Branch, Collaborative Innovation Center of
Assessment toward Basic Education Quality, Beijing Normal
University, Beijing, China
Brain Imaging and Behavior
DOI 10.1007/s11682-016-9530-9
attention and excessive praise from their parents and grand-
parents (Cai et al. 2012; Wang 1984), which may cause unde-
sirable personality traits in the children, such as dependency,
selfishness and social ineptitude (Blake 1981; Cai et al. 2012;
Fletcher 2014;Polit1982). Additionally, due to the absence of
siblings, only-children usually miss out on important opportu-
nities to rehearse some of the more complicated aspects of
relationships within a safe environment and also miss many
opportunities to develop psychosocial skills, emotional support
and learning opportunities compared with non-only-children
(Dunn and Slomkowski 1992; Fletcher 2014). Altogether, the
previous studies mainly focus on the difference in the behavior
(such as intelligence,creativity and personality) between only-
children and non-only-children (Gaynor and Runco 1992;
Polit and Falbo 1987). However, to date, whether the different
family environments (only-child and non-only-child) influence
brain structure development is unclear.
With the development of cognitive neuroscience, increasing
numbers of researchers have focused on the neural mechanisms
associated with inter-individual differences in human psycho-
logical traits, particularly intelligence, creativity and personality
(DeYoung et al. 2013; Silvia et al. 2008; Wei et al. 2014a). For
example, previous studies have shown that creativity is associ-
ated with the structure and function of the frontal lobe (Takeuchi
et al. 2012), primarily the ventrolateral prefrontal cortex, dorso-
lateral prefrontal area, the medial prefrontal cortex and ventro-
medial prefrontal cortex (Berkowitz and Ansari 2008; Chávez-
Eakle et al. 2007;Sawyer2011), and the temporo-parietal re-
gions, including the angular gyrus and supramarginal gyrus
(Arden et al. 2010; Kleibeuker et al. 2013). Regarding intelli-
gence, many studies have suggested that intelligence is associ-
ated with brain structures that are mainly localized to the frontal
regions, parietal regions and BA37/19 (Duncan et al. 2000;Gray
et al. 2003; Haier et al. 2004). Many studies have found that
personality traits are associated with brain structures, primarily
those in the temporal lobe, parietal cortex, temporoparietal junc-
tion, prefrontal cortex, cingulate cortex and hippocampus or
parahippocampal gyrus (DeYoung et al. 2010; Sampaio et al.
2014;SaxeandPowell2006).
Young adulthood is a period of considerable opportunity
and challenge. Many young adults move away from the
homes of their parents and, for the first time, begin college.
Different family environments (i.e., only-child and non-only-
child environments) affect the child’s cognition in terms of
intelligence, creativity, and personality characteristics
(Fletcher 2014; Gaynor and Runco 1992). These influences
of different family environments may cause young adults to
exhibit different adaptabilities to novel college life. It is well
known that general intelligence is an important human quan-
titative trait that accounts for much of the variation in diverse
cognitive abilities and is strongly associated with many im-
portant life outcomes, including educational and occupational
achievements, income, health and lifespan (Davies et al.
2011). Personality traits are stable psychological features and
influence a person’s behavior, emotion, motivation, and cog-
nition (DeYoung et al. 2010), and creativity is known to serve
as a pillar of society that affects all aspects of human life and
plays a crucial role in cultural life (Fink et al. 2010).
Additionally, structural imaging studies are particularly useful
for investigating anatomical correlates of personal character-
istics that involve a wide range of behaviors and ideas that
occur outside the laboratory, such as cognition and personality
(W. Li et al. 2014). Thus, it is very meaningful to investigate
different behaviors, primarily related to intelligence, person-
ality and creativity, and the different structural bases between
only-children and non-only-children in a young adult sample.
Thus, in the present study, we primarily investigated the
anatomical structural differences (i.e., gray matter volume,
GMV) using voxel-based morphometry (VBM) between
only-children and non-only-children. The general intelligence
was measured with the Combined Raven’s Test (CRT), per-
sonality was measured with the revised Neuroticism-
Extraversion-Openness Personality Inventory (NEO-PI-R),
and the creativity of the individuals was tested with the well-
established Torrance Test of Creative Thinking (TTCT) verbal
creativity test. Based on previous studies, we hypothesized
that there would be differences in intelligence, personality
traits and creativity between the only-children and the non-
only-children. Additionally, structural brain differences be-
tween the only-children and the non-only-children may be
present in some brain regions associated with general intelli-
gence, personality characteristics and creativity.
Method
Participants
The participants were college students from our ongoing pro-
ject examining the associations between brain imaging results,
creativity, and mental health. The participants were screened
to confirm healthy development with a self-report question-
naire before scanning, and thus the participants with histories
of psychiatric or neurological disorders and those who had
received mental health treatment or had taken psychiatric
medications were excluded. To control the effect of the differ-
ent nationalities and family structure on the results, a total of
303 subjectsfrom the Han population and two-parent families
completed the MRI scanning and the basic family informa-
tion. All participants were undergraduate or graduate students
from the local community of Southwest University, China.
The participants were all right-handed, healthy individuals
and included 126 only-children (65 male) and 177 non-only-
children (74 male). In the present study, only 270 (108 only-
child) of the 303 subjects finished the creativity and general
intelligence tests, and 249 (95 only-child) of the 303 subjects
Brain Imaging and Behavior
finished the personality trait scale. The greater number of non-
only-children in the present study may because more partici-
pants in our study sample are non-only-children. All partici-
pants provided written informed consent prior to the study.
The Brain Imaging Center Institutional Review Board of
Southwest China University approved this study and the ex-
perimental procedure, which was in accordance with the stan-
dards of the Declaration of Helsinki (1991).
Study measures
Annual family income
The data were collected using the following discrete variables:
1, annual income< Ren Min Bi (RMB) 5000; 2, annual in-
come RMB 5000–15000; 3, annual income RMB 15001–
30000; 4, annual income RMB 30001–50000; 5, annual in-
come RMB 50001–100000; and 6, annual income > RMB
100000. The values of 1–6 were used in subsequent regression
analyses (Takeuchi et al. 2014).
The educational qualifications of both parents
There were five options [1, elementary school graduate or
below; 2, junior high school graduate; 3, high school graduate;
4, university graduate; and 5, above university graduate], and
each choice was converted into the number of years of educa-
tion according to the Chinese education system (i.e., 1, 6 years;
2, 9 years; 3, 12 years; 4, 16 years; and 5, 19 years). The
averages of the converted values for each parent were used
in the analyses (Takeuchi et al. 2014).
Assessment of general intelligence
To examine intellectual ability, the participants completed the
Combined Raven’s Test (CRT), which is a recognized intelli-
gence test with high degrees of reliability and validity (Tang
et al. 2012). The reliability coefficient was 0.92 (Ming 1989).
The CRT, including the Raven’s standard progressive matrix
(the C, D, and E sets) and the Raven’s colored progressive
matrix (the A, AB, and B sets), consisted of 72 items as re-
vised by the Psychology Department of East China Normal
University in 1989. The score for this test (i.e., the number of
correct answers given in 40 min) was used as a psychometric
index of individual intelligence (Wei et al. 2014b). In line with
standard practice, the current study focused on the total score
of the test (Jaeggi et al. 2008; Takeuchi et al. 2011).
Measurements of personality traits
In the current study, the personality traits were assessed using
the revised Neuroticism-Extraversion-Openness Personality
Inventory (NEO-PI-R) (Costa and McCrae 1992). The NEO-
PI-R consists of 240 items and is based on a 5-factor (i.e.,
Neuroticism, Extraversion, Openness, Agreeableness, and
Conscientiousness) model of personality. All of these factors
are divided into 6 subscales. Each factor consists of 48 items,
and the items are answered on a five-point Likert scale that
ranges from strongly disagrees to strongly agree. Previous
studies have shown that the NEO-PI-R has good reliability
and validity (Costa and McCrae 1992; McCrae 2011).
Measuring the level of creativity
Creativity was assessed using the Torrance Tests of Creative
Thinking (TTCT, Torrance 1987). The TTCT was designed as
a measure of divergent thinking, which is a central aspect of
creativity (Huang et al. 2013). The TTCT contains verbal,
figural and auditory tests. In this study, the verbal TTCT was
used to assess individual divergent thinking abilities (Kim
et al. 2006; Torrance 1987). The verbal TTCT comprises sev-
en tasks and for each task scoring comprised three compo-
nents: fluency, flexibility and originality. The total score of
the TTCT is the sum of the fluency, flexibility and originality
score. For a detailed description of the TTCT, please see our
previously published paper (Wei et al. 2014a).
MRI data acquisition
A 3.0-T Siemens Trio MRI scanner (Siemens Medical, Erlangen,
Germany) was used to obtain MR images. A magnetization-
prepared rapid gradient echo (MPRAGE) sequence was used
to acquire high-resolution T1-weighted anatomical images (rep-
etition time = 1900 ms, echo time = 2.52 ms, inversion
time = 900 ms, flip a ngle = 9 deg, resolution matrix = 256 × 256,
slices = 176, thickness = 1.0 mm, voxel size = 1 × 1 × 1 m m
3
).
Preprocessing of structural data
The MR images were processed using SPM8 (Wellcome
Department of Cognitive Neurology, London, UK) imple-
mented in MATLAB 7.8 (MathWorks Inc., Natick, MA,
USA). First, each MRI image was displayed in SPM8 to fil-
trate gross anatomical abnormalities. For more accurate image
registration, the reorientation of the images was manually
fixed to the anterior commissure. The New Segment
Toolbox from SPM8 was applied to every T1-weighted MR
image to extract tissue maps corresponding to gray matter,
white matter, and cerebral spinal fluid in native space.
Subsequently, we performed diffeomorphic anatomical regis-
tration through exponentiated Lie (DARTEL) algebra in
SPM8 for registration, normalization, and modulation
(Ashburner 2007). The DARTEL registration involves, first
computing the specific template using the average tissue prob-
ability maps from all the participants, and followed by
Brain Imaging and Behavior
warping each participant’s segmented maps into specific tem-
plate. The segmented images of gray and white matter were
aligned and warped to a template space. To improve the align-
ment and achieve a more accurate inter-subject registration,
the procedure was repeated until a best study-specific template
was generated. To ensure conservation of regional differences
in the absolute amounts of GM, the image intensity of each
voxel was modulated by the Jacobian determinants.
Subsequently, the normalization function in the DARTEL
toolbox was used to normalize the individual images of gray
and white matter to MNI space (1.5 mm isotropic voxel).
Finally, the normalized modulated images (GM images) were
smoothed with a 10-mm full-width-at-half maximum
Gaussian kernel to increase the signal-to-noise ratio.
Statistical analyses
All of the behavioral data were analyzed with SPSS 16.0. The
statistical analyses of the brain imaging data were performed
using SPM8. In the whole-brain analyses, the two-sample t-
test analysis was used to explore the differences in GMV
between the only-child and the non-only-child.In the analyses
of gender, age and total grey matter volume were included as
nuisance covariates to remove potential confounds. We also
applied explicit masking using the population-specific
masking toolbox in SPM8 to restrict the search volume to gray
matter and white matter. This approach was used instead of
absolute or relative threshold masking to reduce the risk of
false negatives caused by overly restrictive masking in which
potentially interesting voxels are excluded from the statistical
analysis (Ridgway et al. 2009).
Next, in the whole-brain analyses, multiple regression anal-
ysis was used to explore the association between GMV and
individual differences in the behaviors that were found to be
significantly different between the only-children and non-on-
ly-children. The behaviors were used as the variables of inter-
est, and the total brain GMV, age and sex were entered as
covariates of no interest to control for the possible effects of
these variables. We also applied explicit masking using the
population-specific masking toolbox in SPM8 to restrict the
search volume to gray matterand white matter. To increasethe
power to detect brain structural difference between only-child
and non-only-child, statistical analyses focused on key brain
structures associated with creativity, intelligence and person-
ality (DeYoung et al. 2010; Gaynor and Runco 1992;Haier
et al. 2004;Sawyer2011). Thus, a small volume correction
(SVC) was applied to correct for multiple comparisons in the
present study (Becker et al. 2015; Worsley et al. 1996). Based
on previous studies these brain regions: ventrolateral prefron-
tal cortex, dorsolateral prefrontal area, medial prefrontal cor-
tex, ventromedial prefrontal cortex, angular gyrus,
supramarginal gyrus, temporal lobe, parietal cortex,
temporoparietal junction, cingulate cortex hippocampus and
parahippocampal gyrus associated with the creativity, intelli-
gence and personality were choose as the regions of interest
(Arden et al. 2010; Berkowitz and Ansari 2008; DeYoung
et al. 2010;Grayetal.2003; Sampaio et al. 2014).
Structural regions of interest were defined using the WFU
Pickatlas Toolbox (Maldjian et al. 2003). Between-group dif-
ferences within the a priori regions of interest were computed
using a threshold of P < 0.05 (fam ily-wise error-corrected,
FWE; minimum cluster size > 50 voxels).
Results
Behavioral results
Tables 1,2and 3lists the demographics of the only-children
and non-only-children. As indicated in Tables, there were no
significant differences in age or general intelligence between
the only-children and the non-only-children. However, the
only-children exhibited significantly higher family incomes
(t = 2.69, p= 0.008), parental years of education (t = 4.47,
p< 0.001) and creativity scores (flexibility scores) (t= 3.14,
p= 0.002) and significantly lower agreeableness scores
(t = −3.27, p= 0.001) compared with the non-only-children.
MRI results
After entering age, sex, family income, parental education
years and global gray matter volume as covariates, a two-
sample t-test analysis revealed that the GMVs in some clusters
exhibited significant differences between the only-child and
the non-only-child. These clusters primarily included the
supramarginal gyrus (x = 66, y = −18, z = 32, cluster size
=275 voxels, t= 3.94, P =0.015 (corrected); see Fig. 1a), the
mPFC (x= 2, y= 52, z =0, cluster size= 103 voxels, t =−3.90,
P= 0.037 (corrected); see Fig. 1b) and the parahippocampal
gyrus (x = −28,y=1,z=−33, cluster size= 97 voxels,
t=−3.93, P= 0.024 (corrected); see Fig. 1c). Together, the
whole brain t-test revealed that the only-child had greater
Tabl e 1 The demographic characteristics of the all of the only-children
and non- only-children samples (n=303)
Items Only-child
(n=126)
Non-only child
(n=177)
Age (years) 19.81 ± 1.13 19.97 ± 1.18
Family income 3.83 ± 1.36 3.42 ± 1.25**
Parental education years 10.73 ± 3.26 9.29 ± 2.34**
a
Pearson bivariate correlations, shown are r-values
*P<0.05, **P< 0.01
Brain Imaging and Behavior
supramarginal gyrus volumes and smaller mPFC and
parahippocampal gyrus volumes than the non-only-child.
To test whether these different brain structure regions were
associatedwith the differences in the behaviors between only-
children and non-only-children, we saved our results for the
clusters in which significant differences were observed
(mPFC, parahippocampal gyrus and supramarginal gyrus) be-
tween the two samples as ROIs and subsequently extracted the
three ROI signals (the volumes of the ROIs) from each partic-
ipant using the SPM8 toolbox. Next, we tested the relation-
ships between the volume of the three brain regions and the
behaviors, primarily including creativity and agreeableness,
using SPSS 16.0. After controlling for age, sex and total gray
matter volume, the results revealed that the flexibility score
was positively correlated with the volume of the
supramarginal gyrus (for all subjects, r = 0.231, p<0.001; for
the only-child, r = 0.165, p= 0.088; and for the non-only-child,
r = 0.223, p=0.004; see Fig. 2a), and the agreeableness per-
sonality scores were significantly positively correlated with
the volume of the mPFC (for all subjects, r =0.232,
p< 0.001; for the only-child, r = 0.180, p=0.081; and for the
non-only-child, r = 0.203, p= 0.012;see Fig. 2b).
To test whether the flexibility and agreeableness personal-
ity scores were really associated with the supramarginal gyrus
and mPFC, multiple regression analysis was applied to the
whole-brain analysis. After entering the age, sex, global gray
matter volume and general intelligence as covariates and the
flexibility score as the covariate of interest into the regression
model, multiple regression analysis revealed that the creativity
scores were significantly positively correlated with GMV in
the right supramarginal gyrus (x= 69, y= −21, z= 27, cluster
size =210 voxels, t = 4.34, P= 0.02 (corrected); see Fig. 3a).
After entering age, sex and global gray matter volume as co-
variates and agreeableness as the covariate of interest into the
regression model, the multiple regression analysis revealed
that the agreeableness scores were significantly and positively
correlated with the GMV in a cluster that mainly included
areas in the mPFC (x =−11, y = 45, z = 15, cluster size
=149 voxels, t = 3.92, P= 0.036 (corrected); see Fig. 3b).
Additionally, the overlap mapping suggested that there were
common clusters of the different brain structures and a brain
structure associated with behavior (i.e., flexibility and agree-
ableness) between the only-children and non-only-children
(see Fig. 4).
Tabl e 2 The demographic characteristics of the only-children and non-
only-children who finished the creativity and general intelligence tests
(n=270)
Items Only-child
(n=108)
Non-only child
(n=162)
Age (years) 19.70 + ±1.13 19.88 ± 1.13
Family income 3.85 ± 1.35 3.40 ± 1.22**
Parental education years 10.63 ± 3.17 9.11 ± 2.21**
Fluency 52.68 ± 24.59 51.64 ± 18.46
Flexibility 33.00 ± 11.03 28.93 ± 10.00**
Originality 45.70 ± 18.15 44.30 ± 14.79
Total creativity score 131.38± 43.15 124.88 ± 34.19
General intelligence 66.23 ± 3.91 65.26 ± 4.30
a
Pearson bivariate correlations, shown are r-values
*P<0.05, **P< 0.01
Tabl e 3 The demographic characteristics of the only-children and non-
only-children who finished the personality trait scale (n=249)
Items Only-child (n= 95) Non-only child (n=154)
Age (years) 19.76 ± 1.16 19.86 ± 1.17
Family income 3.92 ± 1.28 3.32 ± 1.20**
Parental education years 10.95 ± 3.37 9.16± 2.19**
Neuroticism 137.58 ± 17.60 140.06 ± 19.67
Extraversion 154.43 ± 17.58 154.29 ± 17.87
Openness 159.61 ± 16.08 157.90 ± 14.73
Agreeableness 163.68 ± 11.63 168.84 ± 12.36**
Conscientiousness 161.62 ± 17.35 163.36 ± 18.55
a
Pearson bivariate correlations, shown are r-values
*P<0.05, **P< 0.01
Fig. 1 The brain regions with differences between the only-children and
the non-only-children after controlling age, sex, family income, parental
education years and total gray matter volume (the results areshown with a
threshold of P<0.001 uncorrected for a display purpose). a, the only-
children exhibited greater gray matter volume mainly in the
supramarginal gyrus compared with the non-only-children. b, the only-
children had smaller gray matter volumes mainly in the mPFC compared
with the non-only-children. c, the only-children had smaller gray matter
volumes mainly in the parahippocampal gyrus compared with the non-
only-children
Brain Imaging and Behavior
Discussion
The main aim of present study was to investigate the behavior
and anatomical structural differences between only-children
and non-only-children. The behavioral results revealed that
the only-children had higher creativity scores and lower agree-
ableness scores than the non-only-children. The GMV differ-
ences between the only-children and non-only-children oc-
curred mainly in the supramarginal gyrus, which was
positively associated with flexibility scores; the mPFC, which
was positively associated with agreeableness; and the
parahippocampal gyrus.
The behavioral results revealed that the only-children ex-
hibited higher creativity scores and lower agreeableness
Fig. 2 Scatterplot (partial correlations controlling for age, sex, and
whole-brain gray volume) of the different behaviors and mean GMVs
within the significantly different clusters between the only-children and
the non-only-children. The red imaginary line indicates the only-child
sample, the blue imaginary line indicates the non-only-child sample,
and the black full line indicates all subjects in the two samples. a,The
scatterplot of the flexibility score and mean volume of the supramarginal
gyrus. b, The scatterplot of the agreeableness score and mean volume of
the mPFC
Fig. 3 a. The regions of positive associations between GMV and
flexibility after controlling for age, sex, global gray matter volume and
general intelligence (the results are shown with a threshold of P<0.001
uncorrected for a display purpose). This brain region cluster mainly
included the cluster of the supramarginal gyrus. b. The regions of
positive associations between GMV and agreeableness after controlling
for age, sex and global gray matter volume (the results are shown with a
threshold of P< 0.001 uncorrected for a display purpose). This brain
region cluster mainly included the cluster of mPFC
Fig. 4 a. The voxel-based overlap cluster (right supramarginal gyrus) of
the different brain structures and the brain structure associated with
flexibility between the only-children and non-only-children (red
indicates the different brain structures between the two samples, and
yellow indicates the clusters correlated with flexibility; the results are
shown with a threshold of P< 0.001 uncorrected for a display purpose).
b, The voxel-based overlap cluster (mPFC) of the different brain
structures and the brain structures associate with agreeableness between
the only-children and non-only-children (red indicates the different brain
structures between the two samples, and yellow indicates the cluster
correlated with agreeableness; the results are shown with a threshold of
P< 0.001 uncorrected for a display purpose)
Brain Imaging and Behavior
scores than the non-only-children. Creativity refers to the abil-
ity to change existing thinking patterns, break with the pres-
ent, and build something new (Dietrich and Kanso 2010).
Family structure and parental views are potentially very im-
portant for the development of creative abilities (Gaynor and
Runco 1992). The actual influence of family structure can
probably be explained in terms of interactions, expectations,
and opportunities. On the one hand, compared with the non-
only-child families, the parents of only-child families might
expend more time and effort on the only-child and therefore
undoubtedly increase the direct contact opportunities between
the parents and children and the parents’expectations of those
children (S. Li et al. 2013;PolitandFalbo1987). Many stud-
ies have proven that expectations have a strong influence on
cognitive performance, including instance creativity (Shalley
et al. 2009; Tierney and Farmer 2002). Additionally, only-
children might have more opportunities for independent activ-
ity, and independence is strongly related to creative thinking
(Albert and Runco 1988). Agreeableness has been linked to
psychological mechanisms that allow for the understanding of
others’emotions, intentions, and mental states, including em-
pathy, theory of mind, and other forms of social information
processing (Graziano et al. 2007; Nettle and Liddle 2008). As a
result of the absence of siblings and receiving excessive atten-
tion and too much praise from their parents and grandparents,
only-children may develop fewer psychosocial skillsthan non-
only-children(Caietal.2012; Dunn and Slomkowski 1992;
Fletcher 2014). In contrast, because of their bond with their
parents, only-children are expected to exhibit a reduced need to
affiliate with others (Polit and Falbo 1987). Additionally, only-
children must learn at an early age to entertain themselves
through relatively solitary activities, such as reading (Polit
and Falbo 1987). Thus, it is expected that only-children are
less likely to participate in social groups, such as clubs and
churches, than non-only-children. Furthermore, studies have
indicated that when sociability is measured by self-report,
only-children rate themselves as significantly less sociable than
others (Falbo and Polit 1986; Polit and Falbo 1987).
Altogether, different family environments, such as family com-
position, family size, parent–child interactions and parental
expectations, may cause only-children to have higher creativity
scores and lower agreeableness scores than non-only-children.
Previous studies have suggested thatthe supramarginal gy-
rus plays an important role in switching from one task to
another (Bechtereva et al. 2004;Boothetal.2002; Sohn
et al. 2000), imagination (Knauff et al. 2000), and planning
for task solving (Fincham et al. 2002). Additionally, one study
emphasized that the supramarginal gyrus is most likely in-
volved in providing flexibility of thinking and the imagination
necessary for the successful performance of creative tasks
(Bechtereva et al. 2004). Apart from these findings, other
studies have indicated that the supramarginal gyrus is more
active in decision making in cases of low error rates (Paulus
et al. 2002). Indeed, there cannot be any ‘mistakes’in creativ-
ity because there is no unique answer to the solution of a
creative task. Thus, the supramarginal gyrus, which is related
to error detection, may facilitate creativity (Bechtereva et al.
2004; Paulus et al. 2002). Based on these overall previous
studies, it is possible that the observed greater supramarginal
gyrus volumes in the only-children may account for the great-
er flexibility scores compared to the non-only-children.
Some MRI studies have shown that the mPFC plays a key
role in the processing of emotional information and regula-
tion. For example, imaging studies of self-referential encoding
tasks have indicated that the mPFC is activated during the
processing of emotional information (Fossati et al. 2014;
Gusnard et al. 2001;Phanetal.2004). Additionally, studies
have suggested that the agreeableness personality trait, which
was the only personality trait positively associated with the
MPFC and ACC, is a default mode network (DMN) compo-
nent that is associated with social awareness (including the
ability to attribute mental states to others) (Gusnard et al.
2001; Sampaio et al. 2014). Moreover, stronger activity in
the midline core of the DMN has been related to preferential
self-related activity, such as emotional state attribution, per-
sonal significance, motivation to positive reinforcement, and
social cognition (Andrews-Hanna et al. 2010; Sampaio et al.
2014). Together, the agreeableness-related dimensions reflect a
prosocial orientation and the ability to respond to the needs of
others in an empathic manner, and all of these factors are
social-cognitive tasks that are subserved by the midline core
of the DMN (Andrews-Hanna et al. 2010). Thus, smaller volu-
mes of the mPFC, which is associated with emotional infor-
mation and regulation in only-children, may result in lower
agreeableness scores compared to those of non-only-children.
The parahippocampal gyrus in combination with the
temporopolar area, cingulate cortex, orbitofrontal cortex and
insula forms the paralimbic system (Hui et al. 2010;Leung
et al. 2012), and the paralimbic system is an important transi-
tion area that supports communication between the limbic
system and the neocortex (Kiehl 2006; Leung et al. 2012).
Additionally, the paralimbic system plays an important role
in a range of higher-order cognitive affective functions, such
as emotion/mood regulation and self-control (Kiehl 2006;
Leung et al. 2012). Additionally, many studies have suggested
that the parahippocampal gyrus also plays an important role in
emotional memory. For example, some researchers have
found that the parahippocampal gyrus is correlated with
long-term memory for emotional material (Hamner et al.
1999; Kilpatrick and Cahill 2003) and emotional autobio-
graphical memory (Piefke et al. 2005). Additionally, studies
have revealed that abnormalities, such as decreased gray mat-
ter volumes and altered activates in the parahippocampal gy-
rus, are linked with various conditions of emotional dysfunc-
tion, such as depression, bipolar disorder and schizophrenia
(Chen et al. 2011;Gradinetal.2011; Guo et al. 2013). In this
Brain Imaging and Behavior
study, the smaller parahippocampal gyrus volumes in the on-
ly-children may suggest that these children have weaker
abilities of emotional regulation or self-control than the
non- only-children.
Our study is not without limitations, many of which sug-
gest specific directions for future research. First, thesample of
this study mainly consisted of highly educated young adults,
which may be the main reason that we observed no significant
differences between the only-children and non-only-chil-
dren. Therefore, future work should focus on the ado-
lescent period, which is also a crucial period of lifespan.
Second, the VBM analysis represents a more compre-
hensive measure that integrates changes in the cortical
folding and thickness. Other direct measures of brain struc-
tures (e.g., cortical thickness, surface area, the local
gyrification index, etc.) should be used in further studies to
better understand the neural bias differences between only-
children and non-only-children. Finally, in the present study,
we primarily focused on the differences in intelligence, per-
sonality and creativity between the only-children and non-on-
ly-children. Future work should focus on differences in other
behaviors between these two samples.
These limitations notwithstanding, on the neural level, our
results provide the first evidence that there are differences in
anatomical structures mainly in the cluster of the
supramarginal gyrus that may be correlated with flexibility,
in the mPFC cluster that may play an important role in the
agreeableness personality trait, and in the parahippocampal
gyrus cluster that may play an important role in emotional
regulation and self-control between only-child and non-only-
child. Additionally, our results contribute to the understanding
of the neuroanatomical basis of the differences in cognitive
function and personality between only-children and non-only-
children. More broadly, our results may reveal that differences
in the family environments between only and non-only-
children may contribute to the differences in the behavior
and anatomical structures between these two samples.
Compliance with ethical standards
Funding This research was supported by the National Natural Science
Foundation of China (31271087; 31571137, and 31500885), the National
Outstanding young people plan, the Program for the Top Young Talents
by Chongqing, the Fundamental Research Funds for the Central
Universities (SWU1509383), the Natural Science Foundation of
Chongqing (cstc2015jcyjA10106), and a General Financial Grant from
the China Postdoctoral Science Foundation (2015 M572423), the
Research Program Funds of the Collaborative Innovation Center of
Assessment toward Basic Education Quality (2016-06-012-BZK01).
Conflict of interest The authors declare no competing interests.
Ethical approval All procedures performed in studies involving hu-
man participants were in accordance with the ethical standards of the
Brain Imaging Center Institutional Review Board of Southwest China
University and with the standards of the Declaration of Helsinki (1991).
Informed consent Informed consent was obtained from all individual
participants included in the study.
References
Albert, R. S., & Runco, M. A. (1988). Independence and the creative
potential of gifted and exceptionally gifted boys. Journal of Youth
and Adolescence, 18(3), 221–230.
Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., & Buckner,
R. L. (2010). Functional-anatomic fractionation of the brain’sde-
fault network. Neuron, 65(4), 550–562.
Arden, R., Chavez, R. S., Grazioplene, R., & Jung, R. E. (2010).
Neuroimaging creativity: a psychometric view. Behavioural Brain
Research, 214(2), 143–156.
Ashburner, J. (2007). A fast diffeomorphic image registration algorithm.
NeuroImage, 38(1), 95–113.
Bechtereva, N., Korotkov, A., Pakhomov, S., Roudas, M., Starchenko,
M., & Medvedev, S. (2004). PET study of brain maintenance of
verbal creative activity. International Journal of Psychophysiology,
53(1), 11–20.
Becker, B., Wagner, D., Koester, P., Tittgemeyer, M., Mercer-Chalmers-
Bender, K., & Hurlemann, R. et al. (2015). Smaller amygdala and
medial prefrontal cortex predict escalating stimulant use. Brain,
awv113.
Berkowitz, A. L., & Ansari, D. (2008). Generation of novel motor se-
quences: the neural correlates of musical improvisation.
NeuroImage, 41(2), 535–543.
Blake, J. (1981). The only child in America: Prejudice versus perfor-
mance. Population and Development Review,43–54.
Booth, J. R., Burman, D. D., Meyer, J. R., Gitelman, D. R., Parrish, T. B.,
& Mesulam, M. M. (2002). Functional anatomy of intra-and cross-
modal lexical tasks. NeuroImage, 16(1), 7–22.
Burke, M. O. (1956). A search for systematic personality differentiae of
the only child in young adulthood. The Journal of Genetic
Psychology, 89(1), 71–84.
Cai, H., Kwan,V. S., & Sedikides, C. (2012). A sociocultural approach to
narcissism: the case of modern China. European Journal of
Personality, 26(5), 529–535.
Chávez-Eakle, R., Martindale, C., Locher, P., & Petrov, V. (2007).
Creativity, DNA, and cerebral blood flow. Evolutionary and
Neurocognitive Approaches to Aesthetics, Creativity, and the Arts,
209–224.
Chen, C. H., Suckling, J., Lennox, B. R., Ooi, C., & Bullmore, E. T.
(2011). A quantitative meta‐analysis of fMRI studies in bipolar dis-
order. Bipolar Disorders, 13(1), 1–15.
Costa, P. T., & McCrae, R. R. (1992). Neo PI-R professional manual.
Odessa: Psychological assessment resources.
Davies,G.,Tenesa,A.,Payton,A.,Yang,J.,Harris,S.E.,Liewald,D.,Ke,
X.,LeHellard,S.,Christoforou,A.,&Luciano,M.(2011).Genome-
wide association studies establish that human intelligence is highly her-
itable and polygenic. Molecular Psychiatry, 16(10), 996–1005.
DeYoung, C. G., Hirsh, J. B., Shane, M. S., Papademetris, X., Rajeevan,
N., & Gray, J. R. et al. (2010). Testing predictions from personality
neuroscience brain structure and the big five. Psychological Science.
DeYoung, C. G., Weisberg, Y. J., Quilty, L. C., & Peterson, J. B. (2013).
Unifying the aspects of the Big Five, the interpersonal circumplex,
and trait affiliation. Journal of Personality, 81(5), 465–475.
Dietrich, A., & Kanso, R. (2010). A review of EEG, ERP, and neuroim-
aging studies of creativity and insight. Psychological Bulletin,
136(5), 822.
Brain Imaging and Behavior
Duncan, J., Seitz, R. J., Kolodny, J., Bor, D., Herzog, H., Ahmed, A.,
Newell, F. N., & Emslie, H. (2000). A neural basis for general
intelligence. Science, 289(5478), 457–460.
Dunn, J., & Slomkowski, C. (1992). Conflict and the development of
social understanding.
Falbo, T., & Polit, D. F. (1986). Quantitative review of the only child
literature: research evidenceand theory development. Psychological
Bulletin, 100(2), 176.
Falbo, T., Poston, D. L., Ji, G., Jiao, S., Jing, Q., Wang, S., Gu, Q., Yin,
H., & Liu, Y. (1989). Physical, achievement and personality charac-
teristics of Chinese children. Journal of Biosocial Science, 21(04),
483–496.
Feng, X. (1992). Social characteristics of urban one-child families.
Sociological Research, 1,108–116.
Fenton, N. (1928). The only child. The Pedagogical Seminary and
Journal of Genetic Psychology, 35(4), 546–556.
Festini, F., & de Martino, M. (2004). Twenty five years of the one child
family policy in China. Journal of Epidemiology and Community
Health, 58(5), 358–360.
Fincham, J. M., Carter, C. S., van Veen, V., Stenger, V. A., & Anderson, J.
R. (2002). Neural mechanisms of planning: a computational analysis
using event-related fMRI. Proceedings of the National Academy of
Sciences, 99(5), 3346–3351.
Fink, A., Grabner, R. H., Gebauer, D., Reishofer, G., Koschutnig, K., &
Ebner, F. (2010). Enhancing creativity by means of cognitive stim-
ulation: evidence from an fMRI study. NeuroImage, 52(4), 1687–
1695.
Fletcher, C. (2014). Adult reflections on being an ‘only-child’.
Fossati, P., Hevenor, S. J., Graham, S. J., Grady, C., Keightley, M. L., &
Craik, F et al. (2014). In search of the emotional self: an fMRI study
using positive and negative emotional words. American Journal of
Psychiatry.
Gaynor, J. L., & Runco, M. A. (1992). Family size, birth‐order, age‐
interval, and the creativity of children. The Journal of Creative
Behavior, 26(2), 108–118.
Gradin, V. B., Kumar, P., Waiter, G., Ahearn, T., Stickle, C., & Milders,
M. et al. (2011). Expected value and prediction error abnormalities
in depression and schizophrenia. Brain, awr059.
Gray, J. R., Chabris, C. F., & Braver, T. S. (2003). Neural mechanisms of
general fluid intelligence. Nature Neuroscience, 6(3), 316–322.
Graziano, W. G., Habashi, M. M., Sheese, B. E., & Tobin, R. M. (2007).
Agreeableness, empathy, and helping: a person× situation perspec-
tive. Journal of Personality and Social Psychology, 93(4), 583.
Guo, W., Liu, F., Dai, Y., Jiang, M., Zhang, J., Yu, L., Long, L., Chen, H.,
Gao, Q., & Xiao, C. (2013). Decreased interhemispheric resting-
state functional connectivity in first-episode, drug-naive major de-
pressive disorder. Progress in Neuro-Psychopharmacology and
Biological Psychiatry, 41,24–29.
Gusnard, D. A., Akbudak, E., Shulman, G. L., & Raichle, M. E. (2001).
Medial prefrontal cortex and self-referential mental activity: relation
to a default mode of brain function. Proceedings of the National
Academy of Sciences, 98(7), 4259–4264.
Haier, R. J., Jung, R. E., Yeo, R. A., Head, K., & Alkire, M. T. (2004).
Structural brain variation and general intelligence. NeuroImage,
23(1), 425–433.
Hamner, M. B., Lorberbaum, J.P., & George, M. S. (1999). Potential role
of the anterior cingulate cortex in PTSD: review and hypothesis.
Depression and Anxiety, 9(1), 1–14.
Hao, Y., & Feng, X. (2002). The influence of parent–child reIation on the
growth of onIy chiId. Journal of Huazhong University of Science
and Technology (The Humanities and Social Science Edition),
16(6), 109–112.
Huang, P., Qiu, L., Shen, L., Zhang, Y., Song, Z., Qi, Z., Gong, Q., & Xie,
P. (2013). Evidence for a left‐over‐right inhibitory mechanism dur-
ing figural creative thinking in healthy nonartists. Human Brain
Mapping, 34(10), 2724–2732.
Hui, K. K., Marina, O., Liu, J., Rosen, B. R., & Kwong, K. K. (2010).
Acupuncture, the limbic system, and the anticorrelated networks of
the brain. Autonomic Neuroscience, 157(1), 81–90.
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008).
Improving fluid intelligence with training on working memory.
Proceedings of the National Academy of Sciences, 105(19), 6829–
6833.
Jiao, S., Ji, G., & Jing, Q. (1996). Cognitive development of Chinese
urban only children and children with siblings. Child
Development, 67(2), 387–395.
Kiehl, K. A. (2006). A cognitive neuroscience perspective on psychopa-
thy: evidence for paralimbic system dysfunction. Psychiatry
Research, 142(2), 107–128.
Kilpatrick, L., & Cahill, L. (2003). Amygdala modulation of
parahippocampal and frontal regions during emotionally influenced
memory storage. NeuroImage, 20(4), 2091–2099.
Kim, K. H., Cramond, B., & Bandalos, D. L. (2006). The latent structure
and measurement invariance of scores on the Torrance Tests of
Creative Thinking–Figural. Educational and Psychological
Measurement, 66(3), 459–477.
Kleibeuker, S. W., Koolschijn, P. C. M., Jolles, D. D., De Dreu, C. K., &
Crone, E. A. (2013). The neural coding of creative idea generation
across adolescence and early adulthood. Frontiers in Human
Neuroscience, 7.
Knauff, M., Kassubek, J., Mulack, T., & Greenlee, M. W. (2000). Cortical
activation evoked by visual mental imagery as measured by fMRI.
Neuroreport, 11(18), 3957–3962.
Leung, M.-K., Chan, C. C., Yin, J., Lee, C.-F., So, K.-F., & Lee, T. M. et
al. (2012). Increased gray matter volume in the right angular and
posterior parahippocampal gyri in loving-kindness meditators.
Social Cognitive and Affective Neuroscience, nss076.
Li, S., Chen, R., Cao, Y., Li, J., Zuo, D., & Yan, H. (2013). Sexual
knowledge, attitudes and practices of female undergraduate students
in Wuhan, China: The only-child versus students with siblings. PloS
One, 8(9), e73797.
Li, W., Li, X., Huang, L., Kong, X., Yang, W., & Wei, D. et al. Qiu, J.
(2014). Brain structure links trait creativity to openness to experi-
ence. Social Cognitive and Affective Neuroscience, nsu041.
Maldjian, J. A., Laurienti, P. J., Kraft, R. A., &Burdette, J. H. (2003). An
automated method for neuroanatomic and cytoarchitectonic atlas-
based interrogation of fMRI data sets. NeuroImage, 19(3), 1233–
1239.
McCrae, R. R. (2011). Personality theories for the 21st century. Teaching
of Psychology, 38(3), 209–214.
Ming, W. D. Q. (1989). Revision on the combined Raven’s test for the
Rural in China [J]. Psychological Science 5(004).
Nettle, D., & Liddle, B. (2008). Agreeableness is related to social-cogni-
tive, but not social-perceptual, theory of mind. European Journal of
Personality, 22(4), 323.
Paulus, M. P., Hozack, N., Frank, L., & Brown, G. G. (2002). Error rate
and outcome predictability affect neural activation in prefrontal cor-
tex and anterior cingulate during decision-making. NeuroImage,
15(4), 836–846.
Phan, K. L., Taylor, S. F., Welsh, R. C., Ho, S.-H., Britton, J. C., &
Liberzon, I. (2004). Neural correlates of individual ratings of emo-
tional salience: a trial-related fMRI study. NeuroImage, 21(2), 768–
780.
Piefke, M., Weiss, P. H., Markowitsch, H. J., & Fink, G. R. (2005).
Gender differences in the functional neuroanatomy of emotional
episodic autobiographical memory. Human Brain Mapping, 24(4),
313–324.
Polit, D. F. (1982). Effects of family size: a critical review of literature
since 1973. Final report.
Polit, D. F., & Falbo, T. (1987). Only children and personality develop-
ment: a quantitative review. Journal of Marriage and the Family,
309–325.
Brain Imaging and Behavior
Ridgway, G. R., Omar, R., Ourselin, S., Hill, D. L., Warren, J. D., & Fox,
N. C. (2009). Issues with threshold masking in voxel-based mor-
phometry of atrophied brains. NeuroImage, 44(1), 99–111.
Sampaio, A., Soares, J. M., Coutinho, J., Sousa, N., & Gonçalves, Ó. F.
(2014). The Big Five default brain: functional evidence. Brain
Structure and Function, 219(6), 1913–1922.
Sawyer, K. (2011). The cognitive neuroscience of creativity: a critical
review. Creativity Research Journal, 23(2), 137–154.
Saxe, R., & Powell, L. J. (2006). It’s the thought that counts specific brain
regions for one component of theory of mind. Psychological
Science, 17(8), 692–699.
Shalley, C. E., Gilson, L. L., & Blum, T. C. (2009). Interactive effects of
growth need strength, work context, and job complexity on self-
reported creative performance. Academy of Management Journal,
52(3), 489–505.
Silvia, P. J., Winterstein, B. P., Willse, J. T., Barona, C. M., Cram, J. T.,
Hess, K. I., & Richard, C. A. (2008). Assessing creativity with
divergent thinking tasks: exploring the reliability and validity of
new subjective scoring methods. Psychology of Aesthetics,
Creativity, and the Arts, 2(2), 68.
Sohn, M.-H., Ursu, S., Anderson, J. R., Stenger, V. A., & Carter, C. S.
(2000). The role of prefrontal cortex and posterior parietal cortex in
task switching. Proceedings of the National Academy of Sciences,
97(24), 13448–13453.
Takeuchi, H., Taki, Y., Hashizume, H., Sassa, Y., Nagase, T., Nouchi, R.,
& Kawashima, R. (2011). Cerebral blood flow during rest associates
with general intelligence and creativity. [Research Support, Non-
U.S. Gov’t. PLoS One, 6(9), e25532. doi:10.1371/journal.pone.
0025532.
Takeuchi, H., Taki, Y., Sassa, Y., Hashizume, H., Sekiguchi, A., Nagase,
T., Nouchi, R., & Kawashima, R. (2012). Regional gray and white
matter volume associated with Stroop interference: evidence from
voxel-based morphometry. [Research Support, Non-U.S. Gov’t].
NeuroImage, 59(3), 2899–2907. doi:10.1016/j.neuroimage.2011.
09.064.
Takeuchi, H., Taki, Y., Nouchi, R., Sekiguchi, A., Kotozaki, Y.,
Miyauchi, C. M., Yokoyama, R., Iizuka, K., Hashizume, H.,
Nakagawa, S., Kunitoki, K., Sassa, Y., & Kawashima, R. (2014).
Regional gray matter density is associated with achievement moti-
vation: evidence from voxel-based morphometry. [Research
Support, Non-U.S. Gov’t]. Brain Structure and Function, 219(1),
71–83. doi:10.1007/s00429-012-0485-3.
Tang, C., Li, A., Huang, H., Cheng, X., Gao, Y., Chen, H., Li, P., Cheng,
X.-M., & Zuo, Q. (2012). Effects of lead pollution in SY River on
children’s intelligence. Life Science Journal, 9(3), 458–464.
Tierney, P., & Farmer, S. M. (2002). Creative self-efficacy: its potential
antecedents and relationship to creative performance. Academy of
Management Journal, 45(6), 1137–1148.
Torrance, E. P. (1987). Teaching for creativity. Frontiers of Creativity
Research: Beyond the Basics, 189,215.
Wang, N. (1984). The socialization of the only child in China. Paper
presented at the Conference on Child Socialization and Mental
Health. Honolulu: East–west Center.
Wei, D., Du, X., Li, W., Chen, Q., Li, H., & Hao, X et al. (2014). Regional
gray matter volume and anxiety-related traits interact to predict so-
matic complaints in a non-clinical sample. Social Cognitive and
Affective Neuroscience, nsu033.
Wei, D., Yang, J., Li, W., Wang, K., Zhang, Q., & Qiu, J. (2014b).
Increased resting functional connectivity of the medial prefrontal
cortex in creativity by means of cognitive stimulation. Cortex, 51,
92–102.
Worsley, K. J., Marrett, S., Neelin, P., Vandal, A. C., Friston, K. J., &
Evans, A. C. (1996). A unified statistical approach for determining
significant signals in images of cerebral activation. Human Brain
Mapping, 4(1), 58–73.
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