Maternal sensitivity predicts anterior hippocampal functional networks in early childhood

Abstract

Maternal care influences child hippocampal development. The hippocampus is functionally organized along an anterior–posterior axis. Little is known with regards to the extent maternal care shapes offspring anterior and posterior hippocampal (aHPC, pHPC) functional networks. This study examined maternal behavior, especially maternal sensitivity, at 6 months postpartum in relation to aHPC and pHPC functional networks of children at age 4 and 6 years. Maternal sensitivity was assessed at 6 months via the “Maternal Behavior Q Sort (MBQS) mini for video”. Subsequently, 61 and 76 children underwent resting-state functional magnetic resonance imaging (rs-fMRI), respectively, at 4 and 6 years of age. We found that maternal sensitivity assessed at 6 months postpartum was associated with the right aHPC functional networks in children at both 4 and 6 years of age. At age 4 years, maternal sensitivity was associated positively with the right aHPC’s functional connectivity with the sensorimotor network and negatively with the aHPC’s functional connectivity with the top–down cognitive control network. At 6 years of age, maternal sensitivity was linked positively with the right aHPC’s functional connectivity with the visual-processing network. Our findings suggested that maternal sensitivity in infancy has a long-term impact on the anterior hippocampal functional network in preschool children, implicating a potential role of maternal care in shaping child brain development in early life.

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Brain Structure and Function (2019) 224:1885–1895
https://doi.org/10.1007/s00429-019-01882-0
ORIGINAL ARTICLE
Maternal sensitivity predicts anterior hippocampal functional
networks inearly childhood
QiangWang1· HanZhang1· Chong‑YawWee1· AnnieLee1· JoannS.Poh1· Yap‑SengChong2,3· KokHianTan4·
PeterD.Gluckman2,5· FabianYap8· MarielleV.Fortier6· AnneRifkin‑Graboi7· AnqiQiu1
Received: 8 January 2019 / Accepted: 19 April 2019 / Published online: 4 May 2019
© Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract
Maternal care influences child hippocampal development. The hippocampus is functionally organized along an anterior–
posterior axis. Little is known with regards to the extent maternal care shapes offspring anterior and posterior hippocampal
(aHPC, pHPC) functional networks. This study examined maternal behavior, especially maternal sensitivity, at 6months
postpartum in relation to aHPC and pHPC functional networks of children at age 4 and 6years. Maternal sensitivity was
assessed at 6months via the “Maternal Behavior Q Sort (MBQS) mini for video”. Subsequently, 61 and 76 children under-
went resting-state functional magnetic resonance imaging (rs-fMRI), respectively, at 4 and 6years of age. We found that
maternal sensitivity assessed at 6months postpartum was associated with theright aHPC functional networks in children
at both 4 and 6years of age. At age 4years, maternal sensitivity was associated positively with the right aHPC’s functional
connectivity with the sensorimotor network and negatively with the aHPC’s functional connectivity with the top–down
cognitive control network. At 6years of age, maternal sensitivity was linked positively with the right aHPC’s functional
connectivity with the visual-processing network. Our findings suggested that maternal sensitivity in infancy has a long-term
impact on the anterior hippocampal functional network in preschool children, implicating a potential role of maternal care
in shaping child brain development in early life.
Keywords Resting-state fMRI· Maternal sensitivity· Anterior hippocampus· Posterior hippocampus· Functional
networks
Electronic supplementary material The online version of this
article (https ://doi.org/10.1007/s0042 9-019-01882 -0) contains
supplementary material, which is available to authorized users.
* Anqi Qiu
bieqa@nus.edu.sg
1 Department ofBiomedical Engineering andClinical
Imaging Research Center, National University ofSingapore,
4 Engineering Drive 3 Block E4 #04-08, Singapore117583,
Singapore
2 Singapore Institute forClinical Sciences, Singapore117609,
Singapore
3 Department ofObstetrics andGynaecology, Yong Loo
Lin School ofMedicine, National University ofSingapore,
National University Health System, Singapore, Singapore
4 Department ofMaternal–Fetal Medicine, KK
Women’s andChildren’s Hospital, Singapore (KKH),
Singapore229899, Singapore
5 Liggins Institute, University ofAuckland, Auckland1142,
NewZealand
6 Department ofDiagnostic andInterventional Imaging,
KK Women’s andChildren’s Hospital, Singapore (KKH),
Singapore229899, Singapore
7 Office ofEducation Research, Centre forResearch inChild
Development, Office ofEducation Research, National
Institute ofEducation, Nanyang Technical University,
Singapore, Singapore
8 Department ofPaediatrics, KK Women’s andChildren’s
Hospital, Singapore229899, Singapore

1886 Brain Structure and Function (2019) 224:1885–1895
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Introduction
Maternal sensitivity has been defined as the ability to rec-
ognize and respond both effectively and promptly to the
distress and needs of one’s child (Ainsworth etal. 1978). It
is widely associated with infant’s early linguistic (Paavola
etal. 2006), cognitive (Frick etal. 2018), and self-regula-
tion development (Ispa etal. 2017; Frick etal. 2018), as
well as school-age performance (Treyvaud etal. 2016).
Maternal sensitivity is also considered protective, buffer-
ing influences of early adversity on child behavioral and
cognitive development (Faure etal. 2017; Drury 2012).
Currently, much attention has been devoted to the relation-
ships between maternal sensitivity and child cognition and
behavior. Only a few studies (Rifkin-Graboi etal. 2015a;
Wen etal. 2017b) have begun to explore maternal sensi-
tivity’s influence upon functional brain networks in early
childhood, which may, perhaps in turn, bias subsequent
offspring sensory information and cognitive processing.
Converging evidence has documented that the broader
concept of maternal care impacts hippocampal gluco-
corticoid receptors (Liu etal. 1997), hippocampal tran-
scriptome (Weaver etal. 2006), hippocampal plasticity
(Champagne etal. 2008) and function (Bagot etal. 2012;
Nguyen etal. 2015) in animal models. In line with these
findings, human-imaging studies have found that mater-
nal care associates with a larger hippocampal volume in
the preschool period (Luby etal. 2016b). Also, past work
with 6-month-old infants demonstrates the relationship of
maternal sensitivity with the hippocampal volume, as well
as hippocampal functional connectivity with brain regions
involved in cognitive flexibility, such as the dorsolateral
prefrontal cortex (dlPFC), and autobiographical memory,
such as the lingual gyrus (Rifkin-Graboi etal. 2015a).
Nevertheless, some imaging studies revealed negative
association or no association between the aspect of posi-
tive parenting (Rao etal. 2010; Bernier etal. 2019) and
the hippocampal volume in early childhood (Whittle etal.
2014). The specificity of relations between the caregiving
environment and brain development may be influenced by
the timing of exposure and the age of MRI assessment.
One proposed mechanism influencing such neural
changes involves the hypothalamic–pituitary–adrenal
(HPA) axis and stress responsiveness. That is, low mater-
nal sensitivity may be a salient source of stress in off-
spring during early development, and as such may alter
HPA reactivity and eventually children’s emotional experi-
ence and neurocognitive development (Blair etal. 2006).
Importantly, the hippocampus is one of the core brain
regions involved in stress responsiveness and regulation
(Herman etal. 2005). It is also essential for autobiographi-
cal memory and thinking about the future—two processes
critical to the building of cognitive-emotional schemas
that may guide daily life and social relationships (Cabeza
and St Jacques 2007; Hassabis etal. 2007).
The hippocampus is thought to be functionally organ-
ized along an anterior–posterior axis (Poppenk etal. 2013).
A few studies suggested that the anterior hippocampus is
involved in the non-emotional processes such as spatial
memory (Strange etal. 2014; Zeidman and Maguire 2016;
Sapolsky etal. 1985). Moreover, the posterior hippocampus
is engaged to the stress-related traits such as anxiety and
depression (Satpute etal. 2012) and chronic stress (Sapolsky
etal. 1985), as well as is associated with risk of anxiety and
depression (de Geus etal. 2007). Nevertheless, there is a
consensus that the anterior portion of the hippocampus is
thought to reflect motivational processing and information
encoding in episodic memory, whereas the posterior portion
of the hippocampus is responsible for retrieval processing
in episodic memory and spatial cognition (Poppenk etal.
2013; Nadel etal. 2012). In addition, animal and human
studies show a specific role of the anterior hippocampus
in anxiety-related behaviors (Bannerman etal. 2004; Sat-
pute etal. 2012) and stress-related processing via its close
connections with other subcortical structures relevant to the
HPA axis (Bannerman etal. 2004; Fanselow and Dong 2010;
Mahar etal. 2014). This may suggest that stress related to
the caregiving environment has a larger impact upon the
anterior, rather than the posterior hippocampus. Moreover,
young adults with a history of caregiving deprivation and
emotional neglect were found to exhibit greater anterior, but
not posterior, hippocampal activation during processing of
threatening information such as fearful faces (Maheu etal.
2010). In line with these findings, Fanselow and Dong have
reviewed research indicating that the anterior hippocampus
and its neural connectivity are involved in stress, emotion,
and affect from the behavioral, anatomical, and genetic
perspectives. Hence, we hypothesized that the anterior hip-
pocampal circuitry might be predominantly influenced by
maternal care in early childhood. However, it is unclear
which specific anterior hippocampal circuitry could be
influenced by maternal care. Given the previous findings
on the emphasis of hippocampal-sensory and cognitive net-
works in relation to maternal sensitivity in infants (Rifkin-
Graboi etal. 2015a), we would expect that these networks
may continue being influenced by maternal sensitivity in
early childhood.
In this study, we investigated the above hypothesis and
examined the extent to which maternal sensitivity is asso-
ciated with anterior and posterior hippocampal functional
networks in preschool children at 4 and 6years of age using
resting-state functional magnetic resonance imaging (rs-
fMRI). We studied preschool children partly because of the
rapid development of the brain during this period and the
feasibility of imaging children at this young age. Moreover,

1887Brain Structure and Function (2019) 224:1885–1895
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a previous study also suggested that preschool is a sensitive
period for the influence of maternal support on hippocampal
development (Luby etal. 2016a). Moreover, the associa-
tions of maternal behaviors/adversity with child behaviors
and neurodevelopment are sex dependent. Maternal cortisol
during pregnancy was associated with affective problems
and a larger amygdala volume in girls but not in boys (Buss
etal. 2012). Maternal depressive symptoms were linked
with more internalizing behavioral problems in girls than
in boys (Essex etal. 2003) and were associated with the
amygdala structural and functional development in 4-year-
old girls but not boys (Wen etal. 2017a; Soe etal. 2016).
Hence, we examined the interactive effect of sex-by-mater-
nal sensitivity on the anterior and posterior hippocampal
functional networks. We then utilized a reduced model to
independently estimate maternal sensitivity’s influence upon
anterior and posterior hippocampal functional connectivity
when no interaction effects were observed. We expected that
the association of maternal sensitivity with the hippocampal
functional networks might be sex dependent.
Materials andmethods
Participants
This study was approved by the National Healthcare Group
Domain Specific Review Board (NHGDSRB) and the Sing
Health Centralized Institutional Review Board (CIRB).
Written informed consent was obtained from mothers and
the oral informed consent was obtained from children prior
to participation.
Three hundred and forty-two and 398 mother–child dyads
who participated in the prospective Growing Up in Singa-
pore Towards healthy Outcomes (GUSTO) birth cohort
study were recruited for neuroimaging when children were
4 years and 6 years of age, respectively. The details of the
GUSTO cohort can be found in our previous papers (Soh
etal. 2012).
Maternal education level and maternal ethnicity were
obtained from survey questionnaires conducted as part of a
scheduled appointment during the 26th week of pregnancy.
Birth outcomes (gestational age, birth weight, Appearance,
Pulse, Grimace, Activity, and Respiration (APGAR) score
and sex) and pregnancy measures were obtained from hos-
pital records. This study only included children with ges-
tational age ≥ 34weeks, birth weight ≥ 2kg and a 5-min
APGAR score ≥ 9 to avoid potential influences of pregnancy
outcomes upon brain development. Our inclusion criteria
were matched with previously reported criteria (Buss etal.
2012).
Of the 342 subjects who underwent MRI at 4years of
age, 78 subjects had unusable T1 data due to unsatisfactory
image quality, 4 did not meet the inclusion criteria, 119
mothers did not have maternal sensitivity data, 10 mothers
had unusable maternal sensitivity data (i.e., data lost (n = 3),
faulty audio (n = 5), view issue (n = 1), and language issue
(n = 1)), 30 mothers of infants did not complete depression
questionnaires (i.e., EPDS), 2 mothers did not complete
demographic information questionnaires (i.e., maternal edu-
cation) and 38 subjects had large head motion of rs-fMRI
(maximal framewise displacement, FD > 0.5mm). Hence,
the 4-year-old sample in this study included 61 subjects (33
girls and 28 boys).
Of the 398 subjects who underwent MRI at 6years of
age, 81 subjects had unusable T1 data due to unsatisfac-
tory image quality, 7 did not meet the inclusion criteria, 149
mothers did not have maternal sensitivity data, 16 moth-
ers had unusable maternal sensitivity data (i.e., data lost
(n = 3), faulty audio (n = 7), view issue (n = 3), procedural
inconsistency (n = 2), and father–child interaction (n = 1)),
28 mothers of infants did not complete depression question-
naires (i.e., EPDS), 2 mothers did not complete demographic
information questionnaires (i.e., maternal education) and 39
subjects had large head motion of rs-fMRI (FD > 0.5mm).
Hence, the 6-year-old sample in this study included 76 sub-
jects (46 girls and 30 boys). Table1 lists the demographic
information of the two samples that were used in this study.
Maternal sensitivity
A 15-min mother–child interaction was recorded as part of
a 3-h laboratory visit when infants were 6months of age
(± 2weeks). The mother was asked to “interact or play” with
her 6-month-old infant “as she normally would at home”.
The room was equipped with a foldable chair, highchair,
and a mat, but no toys for the first 5min. After 5min, a
standard set of attractive toys and books was brought into the
room. Maternal sensitivity was assessed using the Revised
Mini-A short form of the Maternal Behavioral Q-Sort-V
(Mini-MBQS-V) (Tarabulsy etal. 2009). The Mini-MBQS-
V consists of 25 items, each representing different possible
aspects of sensitive, and inversely, insensitive, maternal
behavior during interaction with an infant. Coders sort the
25 items into piles of 5, ranging from 1 being “least like
the mother”, to 5 being “most like the mother.” Ratings are
then correlated with that of a theoretically constructed pro-
totypical sensitive mother to derive the global sensitivity
score, ranging from − 1 (very much unlike a prototypical
sensitive mother) to 1 (very much similar to a prototypical
sensitive mother). For example, if the mother’s behavior is
very similar to a “prototypically sensitive mother”, coders
might assign values of “5” to cards such as: “Mother builds
on the focus of the baby’s attention” and “Mother responds
to the baby’s distress and non-distress signals even when
engaged in some other activity”. Likewise, when viewing

1888 Brain Structure and Function (2019) 224:1885–1895
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a mother who is very similar to a prototypically sensitive
mother, coders might assign values of “1” to cards describ-
ing insensitive behavior such as, “Mother tends to tune out
and not notice the infant’s bids for attention” and “The con-
tent and pace of the interaction is set by the mother rather
than the baby’s response”. Three Southeast Asian coders
scored the larger GUSTO cohort cases, and the two of whom
who scored the majority of cases were directly trained by the
developers of the Mini-MQS-V coding system. Together, the
local coders were fluent in both English and the predominant
mother tongue languages of Singapore (i.e., Malay, Manda-
rin, and/or Tamil). Training included the scoring of West-
ern and Singaporean tapes. Reliability for MBQS sensitivity
was assessed between the first two coders across 59 cases
(roughly 15% of the GUSTO sample), and between Coder
Three and Coder One and Coder Two, respectively, across
35 and 31 tapes. The Absolute Intraclass Correlation Coef-
ficient (ICC) Single Measures across all three coders for
sensitivity equaled 0.720, and was 0.861 between Coders
One and Two.
Maternal depression scales
We included the scale of maternal depressive symptoms as
a confounding variable as it is negatively associated with
maternal sensitivity (Crockenberg and Leerkes 2003).
Maternal depressive symptoms were assessed using the
Edinburgh Postnatal Depression Scale (EPDS) at 3months
postpartum. The EPDS is a widely used 10-item self-report
scale designed as a screening instrument for maternal
postnatal depression and valid for use in the prenatal and
early postnatal time points (Bergink etal. 2011). Each EPDS
item is scored on a four-point scale (0–3), and items three
and five-through-ten are reverse scored. All item points
are summed for a total score. Higher EPDS scores indicate
higher levels of depressive symptomatology.
MRI acquisition andpreprocessing
Children underwent MRI scans at age of 4.5 years
(± 1months) and 6years (± 2months) using a 3T Siemens
Skyra scanner with a 32-channel head coil at KK Women’s
and Children’s hospital. The image protocols were: (1)
high-resolution isotropic T1-weighted Magnetization Pre-
pared Rapid Gradient Recalled Echo (MPRAGE; 192 slices,
1mm thickness, in-plane resolution 1mm, sagittal acqui-
sition, field of view 192 × 192mm2, matr ix = 192 × 192,
repetition time = 2000ms, echo time = 2.08ms, inversion
time = 877ms, flip angle = 9°, scanning time = 3.5min); (2)
isotropic axial rs-fMRI protocol (single-shot echo-planar
imaging; 48 slices with 3mm slice thickness, no inter-slice
gaps, matrix = 64 × 64, field of view = 192 × 192mm2, rep-
etition time = 2660ms, echo time = 27ms, flip angle = 90°,
scan time of the first run = 5.27min, scan time of the sec-
ond run = 3.19min). The children were required to close
their eyes during the rs-fMRI scan. The practical scanning
procedure was detailed in the Supplementary of (Wen etal.
2017a). Only the first run of rs-fMRI was used in this study.
The image quality was verified immediately after the
acquisition through visual inspection when children were
Table 1 Demographics
SD standard deviation, EPDS Edinburgh Postnatal Depression Scale, APGAR appearance, pulse, grimace, activity, and respiration
Measure 4-year-old sample (N = 61) 6-year-old sample (N = 76)
APGAR score, mean ± SD (min–max) 9.02 ± 0.13 (9–10) 9.0 ± 0.0 (9–9)
Gestational age (week), mean ± SD (min–max) 38.76 ± 1.35 (34.8–40.6) 39.12 ± 1.11 (34.8–41.1)
Birth weight (g), mean ± SD (min–max) 3133.4 ± 434.8 (2265–4390) 3118.2 ± 447.2 (2265–4390)
Sex, male/female 33/28 46/30
Age (year), mean ± SD (min–max) 4.57 ± 0.08 (4.36–4.75) 6.03 ± 0.13 (5.83–6.61)
Maternal sensitivity score, mean ± SD (min–max) 0.25 ± 0.49 (− 0.68–0.85) 0.21 ± 0.44 (− 0.68–0.81)
3-month EPDS score, mean ± SD (min–max) 8.46 ± 8.56 (0–38) 7.53 ± 6.71 (0–32)
Maternal ethnicity, %
Chinese 42.6 40.8
Malay 36.1 36.8
Indian 21.3 22.4
Maternal education, %
Primary school 4.9 4.0
Secondary school 29.5 27.6
Pre-university, diploma or technical course 36.1 36.8
University undergraduate level 26.2 30.3
Above university undergraduate level 3.3 1.3

1889Brain Structure and Function (2019) 224:1885–1895
1 3
still in the scanner. A scan was repeated when the ring
artifact on T1-weighted images was large. The image
was removed from the study if no acceptable image was
acquired after three repetitions.
Structural MRI FreeSurfer was used to segment brain images
into three tissue types, gray matter (GM), white matter
(WM), and cerebrospinal fluid (CSF). Post-processing qual-
ity checks were conducted according to instructions on https
://surfe r.nmr.mgh.harva rd.edu/fswik i/FsTut orial /Troub lesho
oting Data. Non-linear image normalization was achieved
by aligning individual T1-weighted MRI images to the JHU
atlas (Zhang etal. 2014; Mori etal. 2008) via large deforma-
tion diffeomorphic metric mapping (LDDMM) (Tan and Qiu
2016; Du etal. 2011; Zhong etal. 2010). Visual inspection
was conducted to detect any obvious mapping errors.
Rs-fMRI Rs-fMRI data that had more than 10 volumes in a
row with large motion or a checkered-board image appear-
ance were discarded from any further analysis. FSL was
used to process the rs-fMRI scan for slice time correction,
motion correction, skull stripping, and intensity normaliza-
tion. We computed framewise displacement (FD) of a time
series based on the definition given in (Power etal. 2012) to
quantify head motion. In brief, FD was defined as the sum of
the absolute values of the derivatives of the six realignment
parameters. Rotational displacements are converted from
degrees to millimeters by calculating displacement on the
surface of a sphere of radius 50mm (Power etal. 2012). We
excluded rs-fMRI data if one or multiple volumes had frame-
wise displacement (FD) greater than 0.5mm. The mean and
standard deviation values of the maximal FD among the sub-
jects included in this study were 0.181mm and 0.136mm
in the 4-year-old sample and 0.198mm and 0.134mm in
the 6-year-old sample; the range was from 0.033mm to
0.496mm in the 4-year-old sample and from 0.033mm to
0.481mm in the 6-year-old sample. Figure S1 in the Sup-
plementary Material shows the distributions of max FD and
FD of each rs-fMRI volume over all the subjects at 4 and
6years, as well as FD across the time series in subjects
with the lowest and highest motion at 4 and 6years. Linear
regression analysis was performed to partial out six motion
parameters (three translations and three rotation parameters),
global signal, WM and CSF signals. Global signal regres-
sion was carried out to eliminate artifactual variance due to
head motion, known to be a problem in pediatric popula-
tions (Power etal. 2014). Band-pass filtering (0.01–0.08Hz)
was then applied. For each subject, the mean functional
volume was aligned to the corresponding anatomical image
via rigid body alignment. The functional data were finally
transformed to the JHU atlas space via LDDMM obtained
based on the T1-weighted MRI.
Anterior andposterior hippocampal functional
networks
The hippocampus was defined using the Harvard–Oxford
subcortical atlas (Desikan etal. 2006) from the FSL Soft-
ware Library (Smith etal. 2004). Next, the left and right
hippocampi were vertically divided into the anterior (aHPC)
and posterior (pHPC) segments at Y = − 21mm in the MNI
space (Poppenk etal. 2013). As illustrated in Fig.1, we
discarded 2 coronal slices between the anterior and poste-
rior segments along the anterior–posterior hippocampal axis
to avoid signal mixture between the anterior and posterior
hippocampus.
For each subject, the mean time courses of aHPC and
pHPC were calculated and were correlated with the time
courses of the whole brain to construct bilateral aHPC and
pHPC functional connectivity maps. These maps were then
converted to z value maps using Fisher’s r-to-z transforma-
tion and smoothed with a Gaussian kernel with a full width
half maximum of 6mm.
Statistical analysis
We examined associations between maternal sensitivity
and the aHPC and pHPC functional connectivity maps in
children at both 4 and 6years of age using a mixed-effects
FLAME 1 model implemented in FSL. Maternal educa-
tion and ethnicity, age at MRI scan, postnatal maternal
depressive symptoms at 3months postpartum, and FD were
included as covariates. These factors were taken into account
because maternal education and ethnicity have been shown
Fig. 1 Illustration of the anterior and posterior hippocampal seed
regions. The whole hippocampus was divided into the anterior and
posterior section based on the location of uncal apex in the MNI
space (i.e., Y = − 21mm). To avoid contamination effects between the
aHPC and pHPC, a 2-mm coronal slice from each of the two adjacent
ends was removed. L left, aHPC anterior hippocampus, pHPC poste-
rior hippocampus

1890 Brain Structure and Function (2019) 224:1885–1895
1 3
to affect maternal mood (González etal. 2010), and age at
MRI and FD can influence the functional network (Van Dijk
etal. 2012).
In regression analysis, covariates were entered into the
first block of equations. In the second block, mean-centered
maternal sensitivity and sex were entered. The interaction
term, the product of mean-centered maternal sensitivity and
sex, was entered into the third block. When the interactive
effect was not significant, a reduced model, controlling for
the same covariates and sex, examined maternal sensitivity
in relation to the same outcome measures. Statistical results
were determined at a cluster level (z > 3.1, p < 0.001) and at a
family-wise error rate of 0.05 for the correction for multiple
comparisons (Eklund etal. 2016).
Results
Demographics
The 4- and 6-year-old samples did not differ in APGAR score
(t(135) = 0.515, p = 0.608), gestational age (t(135) = − 1.657,
p = 0.100), birth weight (t(135) = 0.199, p = 0.842), postna-
tal maternal depression (t(135) = 0.714, p = 0.477), mater-
nal sensitivity (t(135) = 0.482, p = 0.631), sex (χ2
(1) = 1.899,
p = 0.168), maternal ethnicity (χ2
(2) = 0.794, p = 0.672) and
maternal education (χ2
(4) = 4.058, p = 0.398). The 4- and
6-year-old samples, respectively, had 4 and 2 subjects whose
gestational age was less than 37weeks. Only 35 subjects
had maternal sensitivity as well as both 4-year and 6-year
rs-fMRI data. Due to this small sample at both time points,
we did not examine our results in a longitudinal manner.
Maternal sensitivity did not vary significantly as a func-
tion of postnatal maternal depressive symptoms (r = − 0.005,
p = 0.970), gestational age (r = 0.029, p = 0.823), sex
(t(59) = − 0.651, p = 0.517), and birth weight (r = − 0.033,
p = 0.802) in the 4-year-old sample. Similarly, maternal sen-
sitivity was not significantly associated with postnatal mater-
nal depressive symptoms (r = − 0.142, p = 0.223), gestational
age (r = 0.182, p = 0.115), sex (t(74) = − 0.901, p = 0.371), and
birth weight (r = 0.071, p = 0.545) in the 6-year-old sample.
Associations ofmaternal sensitivity withanterior
andposterior hippocampal functional networks
The interaction between maternal sensitivity and sex did not
predict the bilateral aHPC and pHPC functional networks at
4 or 6years of age. Next, we report results from the reduced
models.
Four year olds Maternal sensitivity during infancy was
positively associated with 4-year-old’s functional con-
nectivity between the right aHPC and the right precentral
gyrus (corrected p = 0.001), the left postcentral gyrus (cor-
rected p < 0.001) and the right postcentral gyrus (corrected
p < 0.001). Maternal sensitivity was negatively associated
with functional connectivity between the right aHPC and
the left dorsolateral prefrontal cortex (dlPFC) (corrected
p = 0.016) (Fig.2). Table2 lists the anatomical coordinates
of these regions in the MNI space and the cluster size of
these findings. These results remained significant even
when the premature subjects (GA < 37weeks, n = 4) were
excluded (see Figure S2 in the Supplementary Material). In
sum, these findings indicated that maternal sensitivity pre-
dominantly predicted aHPC’s functional connectivity with
sensorimotor and top–down cognitive control networks in
4-year-old children.
Six year olds Maternal sensitivity during infancy was posi-
tively associated with theright aHPC functional connec-
tivity and aspects of the visual-processing network, includ-
ing the left calcarine (corrected p < 0.001), r ight calcarine
(corrected p < 0.001), right lingual (corrected p < 0.001),
and left cuneus cortex (corrected p < 0.001) in 6year olds
(Fig.3). The anatomical coordinates and cluster sizes of
these findings are listed in Table2. When the premature
children (GA < 37weeks, n = 2) were excluded, these results
remained significant (see Figure S3 in the Supplementary
Material). Together, these findings suggested that maternal
sensitivity predicted the aHPC’s functional connectivity
with the visual-processing network in 6-year-old children.
No significant findings were observed with regards to
the bilateral pHPC and left aHPC functional networks in
4- or 6-year-old children.
Fig. 2 Influences of maternal sensitivity on the right anterior hip-
pocampal functional network in 4-year-old sample. L left, R right,
dlPFC dorsolateral prefrontal cortex

1891Brain Structure and Function (2019) 224:1885–1895
1 3
Discussion
Here, we observed that maternal sensitivity, assessed at
6months postpartum, predicted theright aHPC functional
networks in children at both 4 and 6years of age. When
children were 4years of age, previously assessed levels
of maternal sensitivity were positively related to the right
aHPC’s functional connectivity with the sensorimotor net-
work and negatively to the right aHPC’s functional con-
nectivity with the top–down cognitive control network.
When children were 6years of age, previously assessed
levels of maternal sensitivity were positively linked to the
right aHPC’s functional connectivity with the visual-pro-
cessing network. Our findings suggested that maternal sen-
sitivity in infancy has a long-term impact on the anterior
hippocampal functional networks in preschool children.
The sensorimotor network is mainly composed of the
precentral and postcentral cortex (Berman etal. 2016). Its
developmental course starts early and peaks at 2–3years
of age (Casey etal. 2005). Around 4years of age, its con-
nections to the hippocampus are greater in the aHPC rather
than pHPC regions (Riggins etal. 2016). The sensorimotor
development in early life is critical and vulnerable to expo-
sure to psychological adversity, such as maternal anxiety
and depression and maternal care (Sale etal. 2009). In the
same GUSTO sample, we previously employed diffusion
tensor imaging (DTI) and reported that antenatal maternal
anxiety predicted variation in the microstructure of the
precentral and postcentral cortex as well as the medial
temporal lobe (Rifkin-Graboi etal. 2015b). Maternal anxi-
ety at 19weeks gestation was associated with gray matter
volume reductions in the medial temporal lobe and the
postcentral cortex in 6–9-year-old children (Buss etal.
2010). Moreover, a preliminary study with a small sam-
ple (n = 20) showed that maternal sensitivity influences
the hippocampal volume and functional organization in
6-month-old infants (Rifkin-Graboi etal. 2015a, b). Fur-
thermore, abnormalities in the sensorimotor functional
network have been identified in children with withdrawn
behavioral problems assessed using child behavior check-
list (CBCL) at early childhood (Wee etal. 2018). These
findings suggest that maternal psychological adversity and
behavior might manipulate the development of the senso-
rimotor systems.
In contrast to the enhanced coupling between the aHPC
and sensorimotor networks, we observed decoupling
between the aHPC and the cognitive control network as a
function of maternal sensitivity. Such aberrant aHPC-pre-
frontal functional connectivity has also been observed in
studies focused upon the association between early adver-
sity (e.g., childhood poverty and childhood maltreatment)
and neurodevelopment in school-age (7–12) children (Barch
etal. 2016; Birn etal. 2014). Our previous study with a
limited sample (n = 20) from the same GUSTO cohort sug-
gested that maternal sensitivity at6months postpartum was
associated with increased coupling between the hippocam-
pus and dlPFC in 6-month-old infants (Rifkin-Graboi etal.
2015a, b). Together, these findings suggest that directional
Table 2 Statistical associations
between maternal sensitivity
(MS) and right anterior
hippocampal functional
networks
Below lists anatomical structures, their coordinates in the MNI space, and cluster size
Positive and negative, respectively, indicate positive and negative correlations between maternal sensitivity
and the right anterior hippocampal functional network
MS maternal sensitivity, dlPFC dorsolateral prefrontal cortex, L left, R right
Sample Effects Anatomy Peak MNI coordinate Cluster
size (vox-
els)
X Y Z
4year olds MS (positive) R Precentral 20 − 28 54 39
L Postcentral − 36 − 34 58 171
R Postcentral 40 − 30 58 95
MS (negative) L dlPFC − 34 52 14 79
6year olds MS (positive) L Calcarine − 22 − 64 6 79
R Calcarine 12 − 68 12 77
R Lingual 20 − 56 2 41
L Cuneus − 8 − 72 20 13
Fig. 3 Influences of maternal sensitivity on the right anterior hip-
pocampal functional network in the 6-year-old sample. L left, R right

1892 Brain Structure and Function (2019) 224:1885–1895
1 3
effects of the early caregiving environment upon the aHPC-
prefrontal functional connectivity may be influenced by age
at assessment. Indeed, our additional analysis (see Figure
S4 in the Supplementary Material) showed that age does
modulate the relationship of maternal sensitivity and the
aHPC’s functional network. Similarly, past research showed
that the aHPC-prefrontal functional connectivity negatively
predicts episodic memory in 4year olds but positively pre-
dicts episodic memory in 6year olds (Riggins etal. 2016).
It is unclear why the relationship of the aHPC-prefrontal
functional connectivity with maternal care or child cognition
shifts from positive to negative association from younger
to older age in early life. Nevertheless, this phenomenon
is not unique to the hippocampus. In typical development,
the amygdala-prefrontal functional organization also shifts
from positive to negative connectivity from childhood to
adolescence (Gee etal. 2013; Gabard-Durnam etal. 2014). It
has been suggested that earlier amygdala development may
drive heavier bottom–up signaling early in life, which accel-
erates the prefrontal development (Gee etal. 2013; Gab-
ard-Durnam etal. 2014). As top–down signaling increas-
ingly emerges over time, the prefrontal cortex plays a role
in regulating signals from the amygdala (Gee etal. 2013;
Gabard-Durnam etal. 2014). This intriguing model may
be applied to the general development of regulatory con-
nections between the amygdala-hippocampal complex and
prefrontal cortex. In our study, this shift happened much
earlier, suggesting that early childhood demarcates as a criti-
cal and malleable period in the hippocampal-cortical forma-
tion, along with increased vulnerability to environmental
influences.
Finally, in the current study, higher levels of maternal
sensitivity were associated with increased functional con-
nectivity between the aHPC and the visual-processing net-
work in 6year olds. The visual-processing network identi-
fied in the present study consisted of the lingual, cuneus
and calcarine cortex. Prior studies have indicated that the
lingual cortex enhances activity during spatial and visual
working memory (Migo etal. 2015), associates with chronic
perceived stress (Veer etal. 2010), and is linked to resilience
to childhood maltreatment (van der Werff etal. 2013). The
calcarine cortex is thought to play a role in the perception
of visual cues for saccades (Lalli etal. 2006) and visual-
mental imagery (Klein etal. 2000), as well as positive affect
processing (Malhi etal. 2007; Park etal. 2010; Killgore and
Yurgelun-Todd 2007). In line with the findings in this study,
our previous study on 6-month infants (n = 20) showed the
positive associations of maternal sensitivity with the func-
tional connectivity between the hippocampus and the vis-
ual-processing network (Rifkin-Graboi etal. 2015a). These
findings suggest the long-term impact of maternal care on
the hippocampal and visual-processing network. However,
further investigation is needed to find out the relationships
among maternal care, hippocampal and visual-processing
functional organization, and child cognitive and behavioral
outcomes.
In our study, we showed that maternal sensitivity selec-
tively affected the anterior but not the posterior hippocampal
functional networks at both 4 and 6years of age. A substan-
tial body of literature supports the associations of maternal
psychological factors and care with children’s socioemo-
tional behavior (Leerkes etal. 2009; Palmer etal. 2018).
And a consensus from the literature suggests the aPHC is
involved in stress regulation, emotion, and affect (Fanselow
and Dong 2010). This evidence together with our findings
may potentially suggest that the aPHC functional organiza-
tion might be vulnerable to maternal care and play a crucial
role in early socioemotional development.
Our study highlighted the importance of the right but not
left aHPC functional organization in relation to maternal
sensitivity. Our previous study with a small sample (n = 35)
also showed the growth of the right hippocampal volume in
the first 6months of life in association with maternal anxiety
(Qiu etal. 2013). The right hippocampal volume was selec-
tively associated with chronic perceived stress (Gianaros
etal. 2007). It is unclear what are underlying mechanisms
for this specific laterality of the hippocampus sensitive to
stress and maternal psychological factors and care. One sug-
gested explanation was that an asymmetric concentration of
stress-related neurotransmitters, such as serotonin, may play
a role in manipulating right-lateralized hippocampal func-
tions (Bremner etal. 1995).
There were several limitations of this study. Head motion
of rs-fMRI is considered a major factor that influences the
rs-fMRI signal. Nevertheless, there is no gold standard to
characterize the potential effects of head motion on the rs-
fMRI signal. This study utilized a global signal regression
to remove micro-motion that cannot be characterized using
motion parameters (e.g., rotation and translation) (Power
etal. 2014). Even though there is a debate that a global
signal regression could induce spurious negative correla-
tion, we performed additional analysis and demonstrated
that the global signal regression at least did not influence
our findings (see Figure S5 in the Supplementary Material).
Moreover, the sample size of this study was relatively large
for a pediatric imaging study at 4 and 6year olds. Nev-
ertheless, the overlap between 4- and 6-year-old samples
was small, limiting our ability to capture longitudinal tra-
jectories. This research, then, calls for further studies of the
relation between maternal sensitivity and the developmental
trajectory of hippocampal functional networks.
In summary, our findings suggest a long-term impact of
maternal sensitivity during infancy upon anterior hippocam-
pal functional networks in early childhood. Our findings sug-
gest a potential role of maternal care in shaping early-life
child brain development and underscore the importance

1893Brain Structure and Function (2019) 224:1885–1895
1 3
of public health efforts to enhance maternal care during
infancy.
Funding This research is supported by the Singapore National
Research Foundation under its Translational and Clinical Research
(TCR) Flagship Programme and administered by the Singapore Min-
istry of Health’s National Medical Research Council (NMRC), Sin-
gapore- NMRC/TCR/004-NUS/2008; NMRC/TCR/012-NUHS/2014.
Additional funding is provided by the Singapore Institute for Clinical
Sciences, Agency for Science Technology and Research (A*STAR),
Singapore Ministry of Education (Academic research fund tier 1;
NUHSRO/2017/052/T1-SRP-Partnership/01), and NUS Institute of
Data Science, Singapore.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
Ethnical approval All procedures performed in studies involving
human participants were in accordance with the ethical standards of
the institutional and national research committee and with the 1964
Helsinki Declaration and its later amendments or comparable ethical
standards.
Informed consent Informed consent was obtained from all individual
participants included in the study.
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