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Human Brain Mapping 36:4622–4637 (2015)
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Negative Childhood Experiences Alter a
Prefrontal-Insular-Motor Cortical Network in
Healthy Adults: A Preliminary Multimodal
rsfMRI-fMRI-MRS-dMRI Study
Niall W. Duncan,1,2,3,4* Dave J. Hayes,5 Christine Wiebking,6 Brice Tiret,7
Karin Pietruska,8 David Q. Chen,5 Pierre Rainville,8 Małgorzata Marja
nska,9
1
7
5
1,2,3,4
Omar Ayad, Julien Doyon, Mojgan Hodaie, and Georg Northoff
1
Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan
Brain and Consciousness Research Center, Taipei Medical University-Shuang Ho Hospital,
New Taipei City, Taiwan
3
Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China
4
Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research,
University of Ottawa, Ottawa, Canada
5
Division of Neurosurgery, Department of Surgery, University of Toronto and Division of
Brain Imaging and Behaviour Systems Neuroscience, Toronto Western Research Institute,
Toronto, Ontario, Canada
6
Cluster of Excellence in Cognitive Sciences, Department of Sociology of Physical Activity and
Health, University of Potsdam, Potsdam, Germany
7
Functional Neuroimaging Unit and Department of Psychology, Universite de Montreal,
Montreal, Canada
8
Faculte de medecine dentaire, Universite de Montreal, Montreal, Canada
9
Center for Magnetic Resonance Research and Department of Radiology,
University of Minnesota, Minneapolis, Minnesota
2
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Abstract: Research in humans and animals has shown that negative childhood experiences (NCE) can
have long-term effects on the structure and function of the brain. Alterations have been noted in grey
and white matter, in the brain’s resting state, on the glutamatergic system, and on neural and behavioural responses to aversive stimuli. These effects can be linked to psychiatric disorder such as depression and anxiety disorders that are influenced by excessive exposure to early life stressors. The aim of
the current study was to investigate the effect of NCEs on these systems. Resting state functional MRI
(rsfMRI), aversion task fMRI, glutamate magnetic resonance spectroscopy (MRS), and diffusion magnetic resonance imaging (dMRI) were combined with the Childhood Trauma Questionnaire (CTQ) in
Additional Supporting Information may be found in the online
version of this article.
N.W. Duncan and D.J. Hayes contributed equally to this work.
Contract grant sponsor: Biotechnology Research Center (BTRC);
Contract grant numbers: P41 RR008079, P41 EB015894, NCC P30
NS057091; Contract grant sponsors: Canadian Institutes of Health
Research, the Michael Smith Foundation
*Correspondence to: Niall W. Duncan, Ph.D., Assistant Professor,
Graduate Institute of Humanities in Medicine, Taipei Medical
C 2015 Wiley Periodicals, Inc.
V
University, 250 Wu-Xin St., Taipei, 11031, Taiwan. E-mail: niall.w.
duncan@gmail.com
Received for publication 3 March 2015; Revised 21 July 2015;
Accepted 5 August 2015.
DOI: 10.1002/hbm.22941
Published online 19 August 2015 in Wiley Online Library
(wileyonlinelibrary.com).
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Brain Correlates of Negative Childhood Experience
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healthy subjects to examine the impact of NCEs on the brain. Low CTQ scores, a measure of NCEs,
were related to higher resting state glutamate levels and higher resting state entropy in the medial prefrontal cortex (mPFC). CTQ scores, mPFC glutamate and entropy, correlated with neural BOLD
responses to the anticipation of aversive stimuli in regions throughout the aversion-related network,
with strong correlations between all measures in the motor cortex and left insula. Structural connectivity strength, measured using mean fractional anisotropy, between the mPFC and left insula correlated
to aversion-related signal changes in the motor cortex. These findings highlight the impact of NCEs on
multiple inter-related brain systems. In particular, they highlight the role of a prefrontal-insular-motor
cortical network in the processing and responsivity to aversive stimuli and its potential adaptability by
C 2015 Wiley Periodicals, Inc.
V
NCEs. Hum Brain Mapp 36:4622–4637, 2015.
Key words: affect; early life stress; brain networks; aversion; resting state; mood disorder
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INTRODUCTION
Negative childhood experiences (NCEs) are prevalent in
society and have been linked to a broad range of mental
health problems in adulthood [Edwards et al., 2003; Kuo
et al., 2011; Nelson et al., 2002; Turecki et al., 2012]. A
growing body of studies indicates that NCEs can lead to
alterations in brain structure and function in both healthy
and clinical populations. For instance, brain imaging studies have correlated NCEs to changes in both task-related
[Grant et al., 2011; Mueller et al., 2010; Thomaes et al.,
2012] and intrinsic (or resting state) brain activity [Bluhm
et al., 2009; McFarlane et al., 2005]. One particular psychometric measure of NCEs, the childhood trauma questionnaire (CTQ) – which has been shown to be a reliable
measure of physical-emotional abuse and neglect in
healthy populations [Paivio and Cramer, 2004] – has been
correlated to alterations in cognitive function [Gould et al.,
2012; Majer et al., 2010], neuroendocrine stress responses
[Carpenter et al., 2011], microstructural white matter [Lu
et al., 2013], as well as resting state [Howells et al., 2012]
and emotion-related [Dannlowski et al., 2012] brain function in healthy humans. These results suggest that NCEs,
even in healthy adults, can predict changes in brain structure, function, and behavior.
The medial prefrontal cortex (mPFC) is the main anterior region of the default-mode resting state network but
is also integral for the processing of many tasks. In particular, recent studies have shown that mPFC rest- and taskrelated brain connectivity and function are correlated to
the response of the hypothalamic-pituitary stress axis
[Kiem et al., 2013] and aversion-related brain activity
[Hayes et al., 2013, 2014] and is likely key in contextualizing emotion-related information [Roy et al., 2012]. For
instance, prior studies in humans and animals have
revealed a common network of aversion-related activity,
which includes the mPFC, insula, motor cortex, posterior
and mid cingulate, thalamus, amygdala, orbitofrontal cortex, nucleus accumbens, and midbrain [Hayes et al., 2011,
2014]. Although there are few studies specifically looking
at the relationship of the mPFC to the rest of this network,
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one recent study has shown that a measure of GABA in
the mPFC is correlated to BOLD activity both within the
mPFC itself as well as in the motor cortex during aversive
responding [Hayes et al., 2013].
Moreover, a history of NCEs in healthy, medication-free,
adults is predictive of reduced resting state functional connectivity and greater blood oxygen level-dependent
(BOLD) task-related deactivations within regions of the
default-mode network, including the mPFC [Philip et al.,
2013a,b]. Biochemically, the function of the mPFC glutamatergic system has been tied to differences in intrinsic brain
activity in humans [Duncan et al., 2011, 2013, 2014] as well
as aberrant neural and behavioral outcomes related to the
effects of early life stress in rodents [Ali et al., 2011; Judo
et al., 2010; Llorente et al., 2012]. Measures of entropy
[Bruce et al., 2009; Richman and Moorman, 2000], which
reflect intraregional functional variability across time, are
of particular interest as the complexity of brain activity
has been shown to alter during development and has been
linked to efficient brain function [Garrett et al., 2013; Misić
et al., 2010]. Together, these data underscore the need to
better describe the relationship between NCEs and the
structure and function of the mPFC in healthy humans.
An investigation of this nature would be especially relevant to prior work showing blunted neural responding to
aversive stimuli and altered affective circuitry in animal
models of early life stress [Howell et al., 2013; Jahng et al.,
2010] and in humans with mood and anxiety disorders
[Etkin and Wager, 2007; Liberzon et al., 2007; Shin et al.,
2004; Shin and Handwerger, 2009; van Tol et al., 2012].
Moreover, the presence of NCEs has been associated with
decreased PFC grey matter and altered PFC white matter
connectivity [Hanson et al., 2012]. Based on dysfunctions
in the default-mode network and emotional brain function
in a range of mental health problems associated with
NCEs, including post-traumatic stress disorder, major
depressive disorder and anxiety disorders, a connection
between aberrant default-mode network development and
mental disorders in adulthood has been proposed [Broyd
et al., 2009; Daniels et al., 2011; Northoff et al., 2011].
The main aim of this study was to investigate the link
between NCEs, measured with the CTQ [Bernstein et al.,
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Duncan et al.
1994; Paivio and Cramer, 2004], and brain structure and
function. To this end, we used a multimodal neuroimaging
approach including a combination of functional magnetic
resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), and diffusion magnetic resonance imaging
(dMRI) to explore whether NCEs were related to glutamate levels, BOLD activity, and white matter connectivity
associated with the mPFC. Importantly, our goal was to
explore the relationship of the mPFC to NCEs and aversive brain activity, not explicitly as a member of the
default-mode network, but as a hub of affective network
activity. Based on the prior animal and human work noted
above, the first hypothesis was that higher CTQ scores,
reflecting more NCEs, would correlate with disruptions in
mPFC resting state activity (i.e. increased signal variability,
or entropy, across time) and decreased glutamate levels.
The second hypothesis was that more NCEs would result
in reduced neural reactivity within the aversion-related
network [Hayes et al., 2011, 2014], noted above, to the
anticipation of aversive stimuli and that this blunted activity would be correlated with disruptions in white matter
connectivity to the mPFC.
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Figure 1.
Overview of study design.
supplementary results all support the findings from this
core group of 12. For these supplementary analyses 20
subjects had usable mPFC MRS data, 19 had usable left
insula (Lins) MRS data, and 15 had usable resting state
data. All participants gave their written informed consent
and were compensated financially. Approval for the study
was obtained from the ethics committees at McGill University and the Universite de Montreal. Independent analyses
of this dataset have been published previously [Duncan
et al., 2013] and [Hayes et al., 2013].
Psychological Scales
METHODS
Twenty-five healthy, right-handed participants (9
female; 22 6 3.9 years) were scanned using MRI and MRS
at two different scan centres (resting state fMRI, rsfMRI,
and diffusion MRI, dMRI – Montreal Neurological Institute, McGill University; aversion fMRI and MRS – Unite
de neuroimagerie fonctionelle, Universite de Montreal).
Aversion fMRI and MRS scans were carried out on the
same day. Siemens 3T Trio scanners were used at both
locations. Mean time between the two scan days was 3.6
days (range 5 1–10 days). Participants were screened for
current or past psychiatric or neurological disorders and
current recreational drug use through a semi-structured
interview. Screening for depression was carried out using
the Beck Depression Inventory, with a cut-off score greater
than or equal to four [Beck et al., 1996]. Participants were
also excluded if they were currently using any medication
other than contraceptives or were heavy alcohol users (i.e.
more than two drinks per day). Participant demographic
details can be found in the Supporting information.
Data from a number of participants were excluded
because of incorrectly completed questionnaires, excessive
head-motion during any modality, drowsiness during rest
[Duncan and Northoff, 2012], or unusable MRS results.
This strict quality control left a final group of twelve participants that completed all imaging modalities and psychological testing (6 female, 23 6 3.5 years), although one
additional subject was removed from the diffusion MRI
analysis because of incomplete data. Beyond the core 12
subjects, analyses combining each relevant modality were
also carried out using the maximal number of participants
where possible (Supporting Information material); these
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Participants completed the CTQ and the Anxiety Sensitivity Index (ASI) [Reiss et al., 1986]. Total CTQ scores
were used throughout, as it has been shown to be a stable
and reliable indicator of NCEs, both in general and in the
specific population studied [Bernstein et al., 1994; Paivio
and Cramer, 2004]. Participants also completed the
Marlowe-Crowne Social Desirability Scale, to determine if
questionnaire responses were influenced by a propensity
to adhere to social norms [Fraboni and Cooper, 1989].
Magnetic Resonance Spectroscopy
Single voxel edited 1H MR spectra were acquired using
the MEGA-PRESS method [Marjanska et al., 2007; Mescher
et al., 1998]. Voxels of interest were located in the mPFC
(48 3 21 3 21 mm3) and the left insula (23 3 48 3
27 mm3) (Supporting Information Fig. 1 for locations). Difference spectra were analysed using LCModel 6.2-1A [Provencher, 1993, 2001] using a basis set that included an
experimentally measured metabolite-nulled macromolecular spectrum from the occipital cortex (average from 10
subjects) and the experimentally measured spectra from
100 mM phantoms of N-acetylaspartate (NAA), creatine, gaminobutyric acid (GABA), glutamate (Glu), and glutamine (Gln) with pH adjusted to 7.2 and at 378C. Only
results with the Cramer-Rao lower bounds (CRLB) below
20% were included in the analysis. Concentrations with
CRLB > 20% were classified as not detected. The mPFC,
centred on the perigenual anterior cingulate cortex, was
the target region for the study and the LIns was used as a
comparative region, given its clear involvement in aversive
responding and our hypotheses that it would show
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Brain Correlates of Negative Childhood Experience
aversion-related BOLD activity without acting as a key
modulator of the network (e.g. its resting activity or neurochemical content would not correlate to changes in other
regions of the network). Moreover, the left insula was chosen over the right as it is known to play a greater role in
affective responding [Duerden et al., 2013]. Metabolites
were expressed in relation to NAA concentrations.
fMRI Parameters and Preprocessing
Resting state functional echo planar image (EPI) scans were
acquired using body coil transmit and a 32-channel headcoil.
Forty-seven slices aligned at 2308 from the AC-PC plane and
covering the whole brain were acquired per volume
(FoV 5 205 3 205 mm2; spatial resolution 5 3.2 3 3.2 3
3.2 mm3; TE 5 25 ms; TR 5 2,270 ms; flip angle 5 908). The first
five volumes were discarded. A high-resolution anatomical
image was also acquired (MPRAGE; spatial resolution 5 1 3 1
3 1 mm3). Aversion task EPI scans were carried out using the
same acquisition parameters as the resting state other than the
inclusion of a gap between each volume acquisition of 400 ms.
This gap was included to allow the collection of shock
response EMG data without the presence of confounding scanner electromagnetic noise [Piche et al., 2010]. The EMG data
were not included in the present analysis.
The processing of fMRI data was carried out with the
FSL suite of tools [Smith et al., 2004]. In preprocessing
steps common to both the rest and aversion tasks, functional images were aligned to the middle volume, slice
time corrected, and high-pass filtered (100 s). Functional
and anatomical images were aligned with the FSL MNI
standard space template using nonlinear transformations.
Resting State Functional MRI
The resting state session consisted of two eyes-open
(EO) and two eyes-closed (EC) periods (2 3 120 s each),
counterbalanced across participants and indicated with
short tones. EO and EC resting state scans were considered separately given prior findings of differences between
the two conditions [Qin et al., 2012]. Participants were
monitored using a simple camera setup to ensure that
they followed the task and to monitor levels of drowsiness
[Duncan and Northoff, 2012].
Individual resting state data was submitted to an independent component analysis from which noise components
(head motion, blood and breathing related signals) were
identified by two independent investigators [Kelly et al.,
2010] and regressed out of the unsmoothed data, along with
the six head-motion parameters. Again, the primary region of
interest was the mPFC MRS voxel, with the LIns MRS voxel
serving as a task-positive comparative region. MRS regions
were individually masked to create grey matter regions-ofinterest (ROIs) from each subject. The binary grey matter
masks were produced by segmenting each subject’s anatomical image and thresholding the resulting grey matter maps at
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a probability of 0.95. mPFC and LIns grey matter ROIs (mean
volume 5 3926 6 1108 mm3; 8492 6 1153 mm3, respectively)
were converted into functional space using nonlinear transformations. The timecourse from each ROI was extracted
from the unsmoothed data for the EO and EC periods.
Mean ROI entropy was calculated using the SampEn toolbox for MATLAB [Richman and Moorman, 2000] by determining the measure for each voxel and then averaging across
the whole ROI. The SampEn method gives a nonlinear characterization of the degree of organisation within a timeseries,
in this case the BOLD signal [Aboy et al., 2007; Hauge et al.,
2011]. This is done by calculating the log likelihood that a
series of points of a length m (at a particular matching threshold, r) will also be the same at a length of m 1 1 [Richman
and Moorman, 2000], as calculated for all sets of points in the
timecourse. An increase in entropy is generally associated
with an increase in signal complexity [Costa et al., 2005].
Timecourses were normalized to have a mean of 0 and
variance of 1 prior to entropy calculation. A template length
of m 5 2 and a matching threshold of 20% of the standard
deviation of the original timecourse (r 5 0.2) was used.
Aversion Task
Participants underwent an aversion task as reported previously [Hayes et al., 2013]. Electric shocks were applied
to the left ankle using parameters reported elsewhere
[Ladouceur et al., 2012; Mailhot et al., 2012]. Stimulation
(10 3 1 ms rectangular pulses; rate 5 333 Hz, train
duration 5 30ms; <25 mA) was administered to the skin
over the retromalleolar path of the sural nerve at the ankle
via custom-made surface electrodes (1cm2; spaced 2 cm
apart) using an isolated DS7A constant current stimulator
(Digitimer, Welwyn Garden City, Hertfordshire, UK) and
triggered by a Grass S88 train generator (Grass Medical
Instruments, Quincy, MA, USA). Stimulation intensity was
individually determined in a mock scanner using the staircase method [Willer, 1977], with shocks during the experiment being applied at an intensity of 120% of the
participant’s pain threshold. The subjective experience of
pain ratings (0–100, where 0 is not painful and 100 is the
worst imaginable pain) averaged 50.65 6 13.26 across all
subjects, consistent with mild to moderate levels of pain
[Mailhot et al., 2012]. Real-time visual inspection of electrodermal activity was used as further validation that the
stimulation was unpleasant.
The event-design task focused on the anticipation of aversion. At the start of each trial subjects were indicated (0.5 s)
as to whether they would receive a shock with 100% certainty (certain aversion), with 33% certainty (uncertain aversion), or whether they would receive no shock (safe). This
indicator was followed by a first anticipation period (4–8 s)
and then a tone presentation period (2 s). Prior to scanning,
two tones were either conditioned to an aversive stimulus
(i.e., white noise startle), or were presented in the absence of
the startling noise (i.e., was neutral). Following the tone was
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Duncan et al.
a second anticipation period (4–8 s), and finally a shock
delivery in the relevant trials. The order of trials was
pseudo-randomized and intertrial intervals (1–3 s) were
interspersed pseudo-randomly amongst trials. Each condition was presented 25 five times over four runs, giving a
total scan time of 50 minutes. Results from the tone conditioning are presented elsewhere [Hayes et al., 2013]. As only
the anticipation period was of interest in the present study,
given its sensitivity to individual differences in aversive
responses [Carlson et al., 2011], a simplified analytical
approach was used, whereby the anticipation periods were
collapsed across tone types leaving only the aversive anticipation periods delineating the certain shock, uncertain
shock, and safe conditions.
Following preprocessing, aversion functional data were
smoothed (5 mm FWHM kernel) and then analyzed using a
standard GLM approach, as implemented in FEAT [Smith
et al., 2004]. All events were convolved with a canonical
double-gamma HRF. Head-motion parameters and the
mean signal from the CSF and white matter were included
as nuisance variables. As the target was aversive responses,
the contrasts of interest were the anticipation of certain aversion (ACA) versus the anticipation of no shock (Safe), [ACA
v Safe], and the anticipation of uncertain aversion (AUA)
versus the anticipation of no shock, [AUA v Safe].
Diffusion Magnetic Resonance Imaging
Diffusion magnetic resonance (dMRI) images were
acquired using an 8-channel phased-array headcoil. They
were acquired using 99 non-collinear directions over an 8
min 51 s period with a single-shot spin-echo echo planar
sequence, using the following parameters: 1.9 mm isovoxel, 64 slices, 128 3 128 matrix, FOV 5 243 3 243 mm2,
TE 5 89 ms, TR 5 8.3 s, Fourier factor 5 6/8, b 5 1,000 s/
mm2, 10 acquisitions with b 5 0, with GRAPPA parallel
reconstruction with an acceleration factor of 2. The FSL v
5.0 suite (FMRIB Software Library, http://fmrib.ox.ac.uk/
C,
fsl) [Smith et al., 2004] and 3D Slicer v 4.3.1 (NA-MICV
http://www.slicer.org) [Fedorov et al., 2012] suite of brain
imaging tools in a Linux environment were used for preprocessing, registration, and analyses. Diffusion-weighted
scans were motion- and eddy-current corrected in FSL and
imported to 3D Slicer for T1 to mean diffusion-weighted
baseline linear registration and visualization, tensor estimation and the creation of scalar maps for fractional anisotropy (FA) at the individual level. As recommended by
others [Leemans and Jones, 2009], we corrected the b-matrices with finite strain correction by averaging the rotational component of the gradient affine transforms and
then applied these to the original b-matrices.
Whole-brain deterministic multitensor tractography was
performed using the eXtended Streamline Tractography,
or XST, algorithm implemented in 3D Slicer [Qazi et al.,
2009]. This approach was chosen over others given its
superior ability to discern dense, crossing, highly angled,
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and long-distance traveling fibres [Descoteaux et al., 2009;
Khalsa et al., 2014; Qazi et al., 2009]. The seed regions for
tractography were the overlapping mPFC, motor cortex,
and left insula regions noted from the combined multimodal neuroimaging analyses (see below). All analyses
were performed in individual-subject space. The grey matter seed regions were transformed from MNI space to
individual space, a Gaussian blur of 1mm was applied so
that the masks expanded slightly to include the surrounding white matter, and then binarized and resampled to
individual diffusion-weighted space.
Multitensor tractography models were generated using
the following parameters: 0.25 mm seed spacing, C1 threshold 5 0.2, tensor fractional 5 0.1, curve radius 5 0.8 radians,
minimal path length 5 10 mm, step size of 1 mm. See Qazi
et al. [2009] for an in-depth description of parameters.
Resulting tracts were then binarised to voxel space (i.e.
assigned a value of 1 if a streamline passed through it and
zero if it did not), and the conjunction of multitensor maps
between the regions (Motor \ mPFC; mPFC \ Insula; Motor
\ Insula) were identified using fslmaths. Connectivity was
inferred by the presence of common voxels between each
ROI pair. A connection between two regions was inferred to
be ‘direct’ when overlapping voxels were found within each
pair of ROIs or was within three voxels of both ROIs, and
was considered ‘not direct’ when at least one ROI did not
have white matter voxels which met these conditions. The
fractional anisotropy of these common maps was extracted
as an indicator of the strength of structural connections
between each pair of ROIs [Ben-Shachar et al., 2007; Khalsa
et al., 2014]. However, it is important to note that the term
‘structural connectivity’ refers to the apparent connections
between these three regions as identified by the multitensor
models, and that biological connectivity is inferred but not
guaranteed [Le Bihan and Johansen-Berg, 2012].
Combination of Measures
The overall multimodal design of the study is outlined in
Figure 1. Overall, the purpose of combining such measures
was to investigate the potential impact of self-reported NCEs
(i.e. CTQ scores) on mPFC structure and function and on
whole-brain aversion-related activity. As such, two lines of
analysis were performed: (1) CTQ was correlated to the
mPFC’s baseline BOLD activity – i.e. resting state or rsfMRI,
which was measured here via entropy—and its potential for
excitation —i.e. which is indirectly measured through MRS
measures of glutamate, the brain’s primary excitatory neurotransmitter; (2) CTQ was correlated to the whole-brain aversion-related BOLD activity (i.e. via the fMRI design outlined
above) and then to the indirect measure of structural connectivity strength (i.e. extracting fractional anisotropy from
white matter pathways identified by multi-tensor tractography) between the three regions identified in the previous
analyses. Although reliant on correlations, this multimodal
approach is currently the best method for linking behavior
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Brain Correlates of Negative Childhood Experience
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TABLE I. Summary of correlation analyses between CTQ scores and (A) anxiety scores, (B) mPFC and left insula
Glx and Glu ratios, and (C) mPFC and left insula resting state measures for eyes-open and eyes-closed rest
Correlation coefficient (95% C.I.), P-value
(A) [inc. age 1 SocDes]
CTQ 3 ASI (n 5 25)
CTQ 3 ASI (n 5 12)
(B) [inc. age]
CTQ 3 Glx/NAA
CTQ 3 Glu/NAA
(C) [inc. age]
CTQ 3 Ent EC
CTQ 3 Ent EO
0.70, P < 0.001
0.53, P 5 0.072
mPFC
Left insula
20.23 (20.74 2 0.33), P 5 0.35
20.64 (20.91 2 0.23), P 5 0.009
0.17 (20.45 2 0.67), P 5 0.67
0.25 (20.35 2 0.71), P 5 0.40
0.80 (0.43 2 0.94), P 5 0.002
0.23 (20.33 2 0.68), P 5 0.41
0.25 (20.35 2 0.71), P 50.40
0.31 (20.54 2 0.65), P 5 0.33
Abbreviations: CTQ, childhood trauma questionnaire; SocDes, social desirability scale; ASI, anxiety sensitivity index; mPFC, medial prefrontal cortex; Glu/NAA, glutamate/NAA; Glx/NAA, glutamate1glutamine/NAA; Ent, sample entropy.
with in vivo measures of brain function, chemistry, and structure in humans.
Relationships between the basic measures (psychological
scores, biochemistry, structural connectivity, and entropy
values) were tested using Pearson correlation or partial
correlation analyses where appropriate. To determine the
relationship between brain responses to the anticipation of
aversion and each of the psychological scores, the entropy
values, and mPFC Glu/NAA, whole-brain regression analyses were carried out at the group level in FSL’s FEAT.
Significance for all analyses was set at P < 0.05. As multiple statistical comparisons were made, steps were taken to
control for this – FDR correction was used in the case of
correlation results (uncorrected P < 0.009) [Benjamini and
Hochberg, 1995] and cluster-based FWE correction for
whole-brain analyses (Z > 2.3) [Woolrich et al., 2009].
RESULTS
Results from the participant group in which all modalities were available (n 5 12) are given here unless stated
otherwise. Supporting results from separate participant
subgroups that maximize the sample size for each component can be found at the end of each respective section
and conform to those from the core group of 12.
As hypothesized, CTQ scores correlated negatively with
mPFC Glu/NAA concentrations (n 5 12), although not with
Glx/NAA levels (Fig. 2A, Table IIB). In contrast, CTQ scores
were not correlated with biochemical measures in the LIns
control region. As anxiety disorders have been linked to
changes in NAA levels in the mPFC [Shin and Liberzon,
2010], NAA levels in the mPFC were correlated with CTQ
scores. No relationship was seen (n 5 20, R 5 0.11, P 5 0.66).
A group of 20 participants had both usable CTQ scores and
usable mPFC MRS results. CTQ scores were negatively correlated with mPFC Glu/NAA (n 5 20, R 5 20.57, P 5 0.009)
but not correlated with mPFC Glx/NAA (n 5 20, R 5 20.14,
P 5 0.57). In the LIns, 19 participants had both CTQ and
MRS data. No relationship between CTQ scores and LIns
Glu/NAA (n 5 19, R 5 20.17, P 5 0.47) or Glx/NAA
(n 5 19, R 5 0.05, P 5 0.84) was seen.
Resting state entropy within the mPFC ROI during the
eyes-closed condition was found to correlate positively with
CTQ scores (Fig. 2B; Table IC). During the EO condition, no
correlation was observed. No correlation was found
between the CTQ scores and entropy in the LIns during
either EO or eyes-closed (Table IC). Fifteen participants had
usable resting-state and CTQ data. A positive correlation
between CTQ scores and mPFC eyes-closed entropy was
seen (n 5 15, R 5 0.72, P 5 0.0039). A trend was seen with
eyes-open entropy (n 5 15, R 5 0.48, P 5 0.08).
CTQ Correlates with mPFC Glutamate and
Resting State Measures
No individual had a subscale score (emotional abuse/
neglect, physical abuse/neglect, sexual abuse) in the
severe range, although the total CTQ scores (37 6 5.1;
range: 28–44) are consistent with subjects reporting low to
moderate levels of NCEs [Lu et al., 2013]. CTQ scores
were positively correlated with ASI scores in the whole
group (n 5 25), controlling for subject age and social desirability scale scores, although this was a trend in the n 5 12
subgroup (Table IA).
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Left Insula and Motor Cortex Activity Correlate
with Aversion BOLD, CTQ, Glutamate, and
rsfMRI
The main results from this section are all illustrated in
Figure 3.
Aversion BOLD and CTQ
The aversion task produced responses consistent with
previous studies [Fig. 3A; Hayes and Northoff, 2011].
Anticipation of certain aversion (ACA) induced strong
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Figure 2.
(A) Correlation between CTQ scores and mPFC Glu concentrations and (B) between CTQ
scores and mPFC entropy during eyes-closed condition, both controlling for subject age. *
denotes PFDR < 0.05. [Color figure can be viewed in the online issue, which is available at
wileyonlinelibrary.com.]
responses in, amongst other regions, the sensorimotor cortex and bilateral insula (Fig. 3A, Supporting Information
Table S2A). Anticipation of uncertain aversion (AUA)
induced similar responses, although of a lesser extent
(Supporting Information Fig. S2A, Table S2B). During
ACA, the response to the anticipation of aversion was
found to correlate negatively with CTQ scores in multiple
areas, primarily the PCC, sensorimotor cortex and left
insula (Fig. 3B, Supporting Information Table S3A). The
BOLD response to AUA was also found to correlate negatively with CTQ scores in the PCC and precuneus (Supporting Information Figure S2B and Table S3B).
Aversion BOLD and biochemistry
Although no correlations were noted at the FWE corrected level, Glu/NAA in the mPFC correlated positively
with the response to ACA (but not AUA) in the sensorimotor cortex and left insula at a more lenient threshold of
0.005, uncorrected (Fig. 3C, Supporting Information Table
r
S3). We believe that a lowered threshold is justified for
this measure, given our a priori hypotheses, the low subject
number, and the lower signal-to-noise ratio inherent in
MRS measurements. Importantly, no other significant correlations were noted with the ACA or AUA maps at either
threshold for any other biochemical measures, including
with mPFC Glx/NAA, LIns Glx/NAA, and LIns Glu/
NAA. Also, none of the other analyses involved reduced
statistical thresholding of any kind.
Aversion BOLD and rsfMRI
Entropy in the mPFC during EC was found to correlate
negatively with ACA responses in the insula and motor
cortex (Fig. 3D, Supporting Information Table S4A). The
response to AUA was found to correlate negatively with
mPFC eyes-closed entropy in the sensorimotor cortex and
precuneus (Supporting Information Fig. S2D and Table
S4B). No relationship was found between the ACA or
AUA BOLD and mPFC eyes-open entropy.
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Brain Correlates of Negative Childhood Experience
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TABLE II. (A) Pearson correlations for mean fractional anisotropy of overlapping white matter voxels identified for
each ROI pair (i.e. Motor \ mPFC; mPFC \ Ins; Motor \ Ins)
% Sig change difference during ACA
Mean FA
Motor \ mPFC
mPFC \ Ins
Motor \ Ins
Motor 2 mPFC (r, p)
mPFC 2 Ins (r, p)
Motor – Ins (r, p)
20.08, 0.81
0.53, 0.09
0.05, 0.88
0.114, 0.74
20.102, 0.77
20.03, 0.94
20.002, 0.99
0.72, 0.012
0.06, 0.87
% Sig change during ACA
mPFC
Insula
Motor
0.038, 0.91
0.675, 0.023
Mean FA
mPFC \ Ins
20.098, 0.77
Mean FA
mPFC \ Ins
CTQ Scores
20.198, 0.56
% signal change difference for the same ROI pair during the anticipation of certain aversion (ACA); (B) the significant mPFC-Ins pair
and the % signal change in each region of the mPFC, insula and motor cortex; (C) the significant mPFC-Ins pair and CTQ scores.
Significant correlations are highlighted in bold
FA 5 mean fractional anisotropy; p 5 p-values; r 5 Pearson’s correlations.
Abbreviations: ASI, anxiety sensitivity index; CTQ, childhood trauma questionnaire; EPI, echo planar image; mPFC, medial prefrontal
cortex; MRS, magnetic resonance spectroscopy; NCEs, negative childhood experiences; rsfMRI, resting state functional MRI
Aversion BOLD and CTQ, biochemistry and rsfMRI
Results from each of the regressions with ACA activity
were combined to visually identify regions in which all
overlap, highlighting the motor cortex and left insula (Fig.
3E). The overlapping regions in the motor cortex and left
insula consisted of 157 voxels (2 3 2 3 2 mm3) and 27
voxels, respectively. To further typify the CTQ effect on
aversion responses in these overlapping regions, signal
changes for the individual conditions (Safe, AUA, and
ACA) were extracted from the motor cortex overlap. These
were then correlated with CTQ scores, showing that the
negative correlation with [ACA > Safe] activity is driven
by a reduced ACA response, rather than increased safe
signal changes (Fig. 3F).
Structural Connectivity Between mPFC and Left
Insula Is Related to Aversive Motor Cortex
Signal Changes
Figure 4A-B shows an example of the multitensor tractography models generated for an individual. All subjects
showed connectivity between the three pairs of ROIs identified by the multimodal (i.e. MRS, fMRI, CTQ) analysis,
resulting in three connectivity maps (i.e. Motor \ mPFC;
mPFC \ Insula; Motor \ Insula). Most subjects also
showed evidence of ‘direct’ connectivity (eight for Motor
\ mPFC; seven each for mPFC \ Insula and Motor \
Insula; see Methods for definition). Connectivity strength
r
(i.e. mean fractional anisotropy) from each subject’s connectivity maps (see Supporting Information Table 6 for
values) was correlated with the difference in percent (%)
signal changes during the ACA condition between each
ROI pair (Table IIA), revealing a correlation between
mPFC \ Insula connectivity strength and the difference in
motor cortex and left insula % signal changes (Table IIA,
Fig. 4C; r 5 0.72, P 5 0.012). Further investigation revealed
that this correlation was driven by the relationship
between mPFC \ Insula connectivity strength and percent
signal changes in the motor cortex (Table IIB, Fig. 4D;
r 5 0.68, P 5 0.023) during the anticipation of aversive
stimuli, but was not related to signals in the left insula
(r 5 0.04, P 5 0.91) or mPFC (r 5 20.10, P 5 0.77) (Table IIB;
Fig. 4D). mPFC \ Insula FA was not correlated with CTQ
scores (Table IIC; r 5 20.198, p 5 0.56); nor did CTQ correlate with FA values extracted from the mPFC \ Motor
(r 5 20.252, p 5 0.45) or Motor \ Insula (r 5 0.161,
P 5 0.64) tractography maps.
DISCUSSION
We used a multimodal neuroimaging approach to investigate the potential relationship between reports of negative childhood experiences (NCEs; measured using the
CTQ) and changes in brain structure and function (Fig. 1).
Consistent with our first hypothesis, higher CTQ scores
(reflecting more NCEs) were found to correlate negatively
with mPFC resting state Glu/NAA levels and positively
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Duncan et al.
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Figure 3.
Brain maps showing (A) the basic contrast of the anticipation of
certain aversion > safe [ACA > Safe]; (B) the negative correlation between [ACA > Safe] contrast values and CTQ scores
(controlling for ASI); (C) the positive correlation between
[ACA > Safe] contrast values and mPFC Glu; (D) the negative
correlation between [ACA > Safe] contrast values and mPFC
entropy; and (E) the overlap, in green, between each of these
maps. Details of safe and certain individual condition percent signal changes from the overlap in the sensorimotor cortex are
r
given, showing that CTQ scores are correlated with a decrease
in Certain signal change (F). Image result threshold is P < 0.05,
FWE corrected (n 5 12), other than [ACA > Safe] vs mPFC Glu/
NAA where it is P < 0.005, uncorrected. Results are shown
superimposed on the study mean anatomical image. * indicates
PFDR < 0.05. Sagittal x 5 4. CTQ, childhood trauma questionnaire; mPFC, medial prefrontal cortex; Glu/NAA, glutamate/
NAA; Ent, sample entropy. [Color figure can be viewed in the
online issue, which is available at wileyonlinelibrary.com.]
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Brain Correlates of Negative Childhood Experience
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Figure 4.
Example of the three deterministic multi-tensor tractography
models generated in an individual, as seen anteriorly (A) and
sagittally (B), seeded from the mPFC (yellow), left insula (white),
and motor cortex (red). The coordinates of the brain slices chosen for illustrative purposes, and which cut through the ROIs,
are x 5 12, y 5 210, z 5 4 in MNI space. mPFC-Insula connectivity is related to aversive signalling in the motor cortex, indicated
by Pearson correlations between (C) connectivity strength (i.e.
mean fractional anisotropy) of the mPFC-insula overlapping
white matter map and the difference in signal changes between
each region pair and (D) correlation between mPFC-insula connectivity and motor cortex activity during the aversive stimulus.
[Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
with mPFC entropy, or signal complexity (Fig. 2). CTQ
scores also correlated with aversion-related brain responses.
These results are additionally in support of our second
hypothesis, when coupled with the finding that two regions
common to all task- and rest-related measures, the left
insula and motor cortex (Fig. 3), are also related to the
strength of mPFC-insula connectivity (Fig. 4). Unexpectedly,
it is interesting that only three regions (i.e. mPFC, LIns, and
motor cortex) were identified here and not additional
regions of the aversion-related network (e.g. posterior cingulate, thalamus, ventral striatum, amygdala), as might be
anticipated. Also unexpected, only the mPFC-insula (and
not the mPFC-motor or motor-insula) connectivity was correlated to aversion-related activity in the motor cortex, and
the FA values from each of the connectivity maps were not
correlated to CTQ scores. These issues are discussed further
below. Taken together, these results suggest that some differences in resting state brain structure and function, as well
as brain reactivity to aversive stimuli, may be related to the
degree of NCEs in a healthy human population.
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NCEs and mPFC Resting State Measures
Increased measures of individual NCEs were related to
lower levels of mPFC Glu/NAA, while no relationship
existed for Glu/NAA in the LIns, implying regional specificity for the mPFC (Fig. 2A; Table IB). Numerous studies in
non-human animals have demonstrated an effect of early
stressors on glutamatergic function in the mPFC [Ali et al.,
2011; Llorente et al., 2012; Matrisciano et al., 2011] – as well
as impacts in many other regions [Neto et al., 2012; Ryan
et al., 2009; Waes et al., 2009]. Exposure to early life stressors
in rats has revealed decreased glutamatergic measures in
the rat mPFC using MRS [Llorente et al., 2012], altered glutamatergic (i.e., NMDA) receptor expression coupled with
NMDA-linked impaired synaptic potentiation [Ali et al.,
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Duncan et al.
2011; Judo et al., 2010; Matrisciano et al., 2011], and reduced
mPFC cell firing rates [Ali et al., 2011]. Our findings are also
in line with a general link between impaired glutamatergic
function in the mPFC and animal models of acute and longterm stress, anxiety, and depression [Gutièrrez-Mecinas
et al., 2011; Harvey and Shahid, 2012; Knox et al., 2010].
However, no previous studies have linked measures of early
life stressors or NCEs and glutamatergic function in healthy
humans, although many studies have investigated glutamate in the context of mood and anxiety disorders with consistent findings of reduced mPFC glutamate levels [Alcaro
et al., 2010; Hettema et al., 2011; Nair and Singh Ajit, 2008;
Riaza Bermudo-Soriano et al., 2012].
Alterations in intrinsic activity, particularly in the so-called
default-mode network, have been linked to mood and anxiety disorders that are also associated with early life stress
[Broyd et al., 2009; Northoff et al., 2011; Sylvester et al., 2012].
The current findings advance this notion by demonstrating
that the degree of NCEs may impact resting state activity
properties in the mPFC – a key region within the defaultmode and affective processing networks [Daniels et al., 2011;
Roy et al., 2012]. Increased instances of reported NCEs were
related to increased measures of mPFC, but not insular,
entropy (Fig. 2B; Table IC). This is consistent with prior findings showing that mPFC entropy measures are related to
autonomic activity at rest [Ziegler et al., 2009] and that higher
entropy or complexity is often, but not always, related to
poorer functioning in mental disorders such as Alzheimer’s
or schizophrenia [Takahashi, 2013]. The link between NCEs,
glutamate, and entropy complement and extend previous
studies showing an effect of early life stressors on EEG frequency bands which predict signal entropy [Bruce et al.,
2009; McFarlane et al., 2005], and prior results demonstrating
an effect of stress on mPFC resting state activity [Molina
et al., 2010; Philip et al., 2013a; Shin and Handwerger, 2009].
In addition, they support the idea that the development of
intrinsic activity is likely important for healthy adult brain
function, and that early life stressors or NCEs may act over
time to constrain the range of variable responses in this
regard [McIntosh et al., 2008; Raja Beharelle et al., 2012; Uhlhaas and Singer, 2011]. It is also important to note that our
findings are in the eyes-closed (and not eyes-open) resting
state period only. Although the mechanism for such a difference is currently unclear, these findings are in line with our
prior findings (Qin et al., 2012) and the recent work of others
[Zhang et al., 2015] revealing clear differences in resting state
activity between these two conditions and controversially
suggesting that the eyes-closed condition may be somewhat
closer to what is meant by a ‘resting state’.
NCEs Are Correlated to an mPFC-Insula-Motor
Cortex Network
Aversion-related brain responses were shown to be negatively correlated with CTQ scores during the anticipation
of both a certain and uncertain shock, suggesting that peor
r
ple who self-report greater NCEs do not show the same
neural reactivity as those reporting fewer NCEs (Fig. 3).
Interestingly, although there is some evidence from the
animal literature that ‘safety’ signals might contribute significantly to this correlation [Christianson et al., 2011; Fernando et al., 2014], we found the relationship between
NCEs and signal changes related only to the aversive
responsivity (Fig. 3F). Although our measure of NCEs negatively correlated with activity throughout the aversion
network [Hayes and Northoff, 2011], the motor cortex and
LIns were identified as key areas when considered in conjunction with mPFC measures of glutamate and signal
complexity (Fig. 3E). Measures of combined mPFC glutamate/glutamine correlate positively with BOLD responses
in task-positive regions [Duncan et al., 2011], fitting with
the relationship seen between mPFC glutamate and
aversion-related responses seen here. Moreover, these data
are also consistent with the role of the mPFC as an integrative region that modulates and contextualizes cognitiveemotional responses to stimuli [Hayes et al., 2014; Roy
et al., 2012]. Interestingly, fractional anisotropy of the
mPFC-insula connectivity, an indirect measure of connection strength, was correlated to aversion-related BOLD
activity in the motor cortex but was not correlated to CTQ
scores, suggesting that the association between NCEs and
connectivity may be mediated by aversion-related activity
in this case. This is not inconsistent with the notion that
mPFC white matter volume may be altered in those with
greater levels of NCEs [Hanson et al., 2012], and in fact
future studies should include measures of NCEs in both
healthy and clinical populations to investigate the possibility of graded effects. It is worth noting that although most
prior work has focused on alterations in the amygdala and
hippocampus in people reporting NCEs [Dannlowski
et al., 2012; Hart and Rubia, 2012], a meta-analysis of
imaging studies on post-traumatic stress disorder found
decreased activation in many of the regions identified
here, including the insula and mPFC [Etkin and Wager,
2007]. Similarly, higher anxiety levels correlate with
reduced activity changes in response to aversive stimuli in
these regions [Drevets et al., 1995; Simpson et al., 2001;
Zhao et al., 2007].
We found the mean fractional anisotropy of the white
matter connection between the mPFC and left insula—an
indicator of structural connectivity strength [Ben-Shachar
et al., 2007; Khalsa et al., 2014]—to be positively correlated
to aversion-related signal changes in motor cortex (Fig. 4).
This finding is in line with prior work showing decreases
in PFC white and grey matter volumes in healthy humans
with more self-reported NCEs [Hanson et al., 2012] as well
as a study on decision-making which showed increased
functional connectivity between the mPFC and left anterior
insula during the processing of affective context [Rudorf
and Hare, 2014]. However, NCEs were not correlated to
mPFC-insula structural connectivity directly, though they
are correlated to insula and motor cortex signal changes,
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Brain Correlates of Negative Childhood Experience
Figure 5.
Overview of study results showing relationship between CTQ
scores, resting state measures, and aversion task responses.
Green arrows denote a positive relationship and red negative.
mPFC, medial prefrontal cortex. [Color figure can be viewed in
the online issue, which is available at wileyonlinelibrary.com.]
suggesting that the relationship between NCEs and mPFCinsular-motor cortex signals is influenced indirectly via
this structural connectivity. It is unclear why connections
between the mPFC-motor cortex and insula-motor cortex
appear unrelated to aversion-related signal changes (Fig. 4;
Table II). However this might be related to the fact that
these connections are mainly indirect through corticostriatal-motor and cortico-thalamic pathways [Guye et al.,
2003; Neggers et al., 2015]. Also, the insula is anatomically
variable [Rosen et al., 2015] and its connections are
regionally-selective (e.g. motor connectivity arises mainly
from the posterior insula), but show generally strong connectivity to prefrontal regions [Cloutman et al., 2011].
Thus, our insular ROI, which included the entire region,
may be better suited for uncovering relationships to the
prefrontal cortex compared to many other regions. Regardless, our low sample size seems an unlikely candidate
given the strength (mPFC-insula), or complete absence
(mPFC-motor; insula-motor), of correlations (Table IIA,B).
Taken together, and in light of prior human and animal
findings on the impact of early life stressors on brain structure
and function, these findings (Fig. 5) support the evidence that
a prefrontal-insular-motor cortex network is fundamentally
involved in the processing of, and responsivity to, aversive
stimuli and that this network can be shaped by NCEs.
LIMITATIONS AND CONCLUSION
There are a number of limitations of the current study.
Firstly, although consistency of these results with those of
the larger subgroups and across independent modalities
point to their robustness, replication with a larger sample
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is essential. Secondly, although one may question the reliability of the self-reporting of NCEs (via the CTQ), prior
evidence into the validity of abuse reports supports their
reliability [Chu et al., 1999]. Similarly, one may question
the validity of concluding brain changes from CTQ scores
in a non-clinical population given that participants were
healthy at the time of the study. However, prior studies
have observed physiological changes correlating with CTQ
scores in healthy subjects [Carpenter et al., 2011; McFarlane et al., 2005], and in conjunction with our own, these
findings may reflect the potential continuum-like impact
of NCEs on brain and behavior in adulthood. Future studies which include both healthy and clinical-level subjects
will be needed to outline this idea further. On the other
hand, it is not possible in our current sample to rule out
relationships between negative experiences in adulthood
with the brain effects seen here, or to consider the possible
impact of individual resiliency levels [Cisler et al., 2012].
Thirdly, although subjects were monitored for drowsiness
during rsfMRI, we did not correlate levels of awareness
with entropy values. Fourthly, given that the subjects did
not rate their level of anxiety for the anticipation of the
impeding shock, it is possible that the correlations seen
here are more related to individual anxiety levels compared to aversive responding per se, especially as anxiety
has been previously tied to NCEs [Huh et al., 2014].
Finally, subjects were recruited from the local student population and so, given the general class and educational
structure of such a group, may not be representative of the
population as a whole. Extending the study into different
social contexts and specific patient populations is thus a
desirable next step.
In conclusion, using a multimodal imaging dataset, it
was shown that reported NCEs correlate with mPFC resting state dynamics and biochemistry, and are related to
altered aversion-related neural responses and structural
connectivity between the mPFC and Lins. Although it is
merely speculation at this point, we suspect that under
most conditions, the following findings likely reflect a
poorer (i.e. nearer to pathological) overall response to
aversive/stressful stimuli: (1) relatively lower aversionrelated network responsivity to unpleasant stimuli, (2)
lower resting state levels of mPFC glutamate, (3) increased
mPFC entropy, and (4) alterations in mPFC white matter
connectivity (though this appears indirect in the present
study). This statement is made in light of the many animal
and human studies (particularly those related to general
aversive responding and anxiety and mood disorders)
noted above. These results provide potential links between
previous animal studies and clinical observations and give
insight into the mechanisms by which early life experiences may impact upon brain function in adulthood.
ACKNOWLEDGMENTS
The authors would like to thank A. Perna and K. Dedovic
for their help with subject recruitment, Edward J. Auerbach,
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Ph.D. (Center for Magnetic Resonance Research, University
of Minnesota) for implementing MEGA-PRESS sequence on
Siemens, the staff at the UNF and MNI for their skillful
assistance, and Romain Valabregue, Ph.D. (Center de NeuroImagerie de Recherche, Paris, France) for developing
processing tools.
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