Neural Correlates of the Cortisol Awakening Response in Humans

Abstract

The cortisol rise after awakening (cortisol awakening response, CAR) is a core biomarker of hypothalamic-pituitary-adrenal (HPA) axis regulation related to psychosocial stress and stress-related psychiatric disorders. However, the neural regulation of the CAR has not been examined in humans. Here, we studied neural regulation related to the CAR in a sample of 25 healthy human participants using an established psychosocial stress paradigm together with multimodal functional and structural (voxel-based morphometry) magnetic resonance imaging. Across subjects, a smaller CAR was associated with reduced grey matter volume and increased stress-related brain activity in the perigenual ACC, a region which inhibits HPA axis activity during stress that is implicated in risk mechanisms and pathophysiology of stress-related mental diseases. Moreover, functional connectivity between the perigenual ACC and the hypothalamus, the primary controller of HPA axis activity, was associated with the CAR. Our findings provide support for a role of the perigenual ACC in regulating the CAR in humans and may aid future research on the pathophysiology of stress-related illnesses, such as depression, and environmental risk for illnesses such as schizophrenia.Neuropsychopharmacology accepted article preview online, 17 March 2015. doi:10.1038/npp.2015.77.

Full text

Neural Correlates of the Cortisol Awakening Response
in Humans
Andreas Boehringer
1
, Heike Tost
1
, Leila Haddad
1
, Florian Lederbogen
1
, Stefan Wüst
1,2
, Emanuel Schwarz
1
and Andreas Meyer-Lindenberg*
,1
1
Central Institute for Mental Health, University of Heidelberg/Medical Faculty Mannheim, J5, Mannheim, Germany
The cortisol rise after awakening (cortisol awakening response, CAR) is a core biomarker of hypothalamic-pituitary-adrenal (HPA) axis
regulation related to psychosocial stress and stress-related psychiatric disorders. However, the neural regulation of the CAR has not been
examined in humans. Here, we studied neural regulation related to the CAR in a sample of 25 healthy human participants using an
established psychosocial stress paradigm together with multimodal functional and structural (voxel-based morphometry) magnetic
resonance imaging. Across subjects, a smaller CAR was associated with reduced grey matter volume and increased stress-related brain
activity in the perigenual ACC, a region which inhibits HPA axis activity during stress that is implicated in risk mechanisms and
pathophysiology of stress-related mental diseases. Moreover, functional connectivity between the perigenual ACC and the hypothalamus,
the primary controller of HPA axis activity, was associated with the CAR. Our findings provide support for a role of the perigenual ACC in
regulating the CAR in humans and may aid future research on the pathophysiology of stress-related illnesses, such as depression, and
environmental risk for illnesses such as schizophrenia.
Neuropsychopharmacology advance online publication, 8 April 2015; doi:10.1038/npp.2015.77
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INTRODUCTION
The hypothalamic-pituitary-adrenal (HPA) axis is the
organism’s most important neuroendocrine stress system
(de Kloet et al, 2005) and plays a crucial role in the
pathophysiology of stress-related mental illnesses, including
major depressive disorder (MDD) (Pariante and Lightman,
2008) and posttraumatic stress disorder (PTSD) (de Kloet
et al, 2006). It has also been implicated in the pathophysiol-
ogy of environmental risk factors for a broader range of
severe mental disorders including schizophrenia and MDD,
such as urban upbringing, childhood abuse, and migration
status (Meyer-Lindenberg and Tost, 2012). HPA axis
function is typically quantified using proxy measurements,
such as the cortisol increase within the first hour after
awakening, also known as the cortisol awakening response
(CAR) (Pruessner et al, 1997; Wilhelm et al, 2007). This
measure of HPA axis reactivity is a longitudinally relatively
stable readout (Wust et al, 2000b) for which a significant
heritability was consistently reported (Wust et al, 2000a;
Bartels et al, 2003; Kupper et al, 2005). The CAR is associated
with psychosocial stress (Chida and Steptoe, 2009) and
predicts current and future incidence of depression
(Pruessner et al, 2003; Huber et al, 2006; Mannie et al,
2007; Vreeburg et al, 2009; Adam et al, 2010; Dedovic et al,
2010; Lamers et al, 2013; Vrshek-Schallhorn et al, 2013). It is
also altered in patients with PTSD (Wessa et al, 2006) and
those with first-episode psychosis (Mondelli et al, 2010),
and has been associated with childhood trauma (Heim et al,
2009; Mangold et al, 2010). Interestingly, the CAR is a
stronger prospective predictor of MDD than other readouts
of HPA axis activity (Adam et al, 2010), suggesting that this
parameter mirrors aspects of HPA axis function particularly
implicated in the risk for psychiatric disorders.
Preclinical work has demonstrated that a key region for
neural control of HPA axis function is the anterior cingulate
cortex (ACC), which has dense anatomical connections to
downstream visceral and emotional sites, such as the amygdala,
the hippocampus, and the hypothalamus (Herman et al,
2005; Ulrich-Lai and Herman, 2009; Etkin et al, 2011). In
rodents, the ACC inhibits HPA axis activity through trans-
synaptic connections to the paraventricular nucleus of the
hypothalamus (Diorio et al, 1993), the primary promoter of
HPA axis activity (Herman et al, 2005), and plays a role in
glucocorticoid-mediated feedback control of stress-related
HPA axis activity (Diorio et al, 1993). In line with this
animal work, the ACC has been implicated in neural control
of stress-related HPA axis activity in a human functional
neuroimaging study (Pruessner et al, 2008), but the mech-
anistic basis of this involvement remains to be elucidated. In
humans, the perigenual division of the ACC (pACC) has also
been implicated in emotion processing, gene–environment
*Correspondence: Professor A Meyer-Lindenberg, Psychiatry and
Psychotherapy, Central Institute of Mental Health, Square J5,
Mannheim 68159, Germany, Tel: +49 621 1703 6519, Fax: +49 621
17032005, E-mail: a.meyer-lindenberg@zi-mannheim.de
2
Current address: Institute of Experimental Psychology, University of
Regensburg, Universitätsstrasse 313, 93053 Regensburg, Germany
Received 12 December 2014; revised 27 February 2015; accepted 6
March 2015; accepted article preview online 17 March 2015
Neuropsychopharmacology (2015), 1–8
©
2015 American College of Neuropsychopharmacology. All rights reserved 0893-133X/15
www.neuropsychopharmacology.org

interactions (Pezawas et al, 2005; Meyer-Lindenberg et al,
2006), the pathophysiology of depression (Pezawas et al,
2005; Hamani et al, 2011), PTSD (Karl et al, 2006),
schizophrenia (Fusar-Poli et al, 2012), and risk factors
relevant to these disorders (Lederbogen et al, 2011; Meyer-
Lindenberg and Tost, 2012; Tost and Meyer-Lindenberg,
2012; Akdeniz et al, 2014).
Here, we hypothesized that the pACC regulates HPA axis
function as measured with the CAR. We combined multi-
modal structural (using voxel-based morphometry, VBM)
and functional magnetic resonance imaging (fMRI) with an
established psychosocial stress protocol (Pruessner et al,
2008) in healthy human participants to test this hypothesis.
MATERIALS AND METHODS
Subjects
Functional and structural MRI data from 25 healthy
participants (mean age ±SD 41.9 ±15.2 years, 14 women
(mean age ±SD 40.6 ±15.7, range 21–68 years) and 11 men
(mean age ±SD 43.5 ±15.3, range 21–67 years)) who took
part in a previously reported study including 36 participants
with a different focus (Lederbogen et al, 2011) and for whom
the CAR has been assessed were reanalyzed to identify neural
correlates of the CAR. One participant from the foregoing
study showed a CAR43 SD above the group mean and was
therefore excluded from the present analyses as an outlier.
All participants were examined by a trained physician who
also obtained a medical history of current or past treatment
of a psychiatric disorder. Exclusion criteria included left-
handedness, a lifetime history of significant general medical,
psychiatric, or neurological illness, prior psychiatric, psy-
chological, or psychotropic pharmacological treatment, and
head trauma. Current clinically relevant depression was
assessed using the Hamilton Depression Rating Scale with
21 Items (HAMD-21, Hamilton, 1960). No participant had
a HAMD-21 score above 4 (mean ±SD 0.88 ±1.2). All
participants gave written informed consent to a study
protocol approved by the ethics committee of the University
of Heidelberg.
CAR Assessment and Quantification
For quantification of basal HPA axis activity, participants
were asked to collect four saliva samples on a regular
weekday (awakening (t
0
), as well as 30 min (t
30
), 8 h (t
8
), and
14 h (t
14
) after awakening). The mean ±SD temporal
distance of the saliva collection from the stress test was
23 ±34 days (before or after the stress test). Subjects were
carefully instructed to refrain from food, drinks other than
water, or brushing their teeth before completion of saliva
sampling. Sampling times and adherence to the sampling
procedure was documented by the participants in a written
protocol. Upon receipt, samples were frozen and stored at
−80 °C. For cortisol analysis, a time-resolved immunoassay
with fluorescence detection was used with coefficients
of intra- and interassay variation of o8% (Dressendorfer
et al, 1992). Subsequently, the CAR was calculated as the
increase in cortisol from the first to the second saliva sample
in nmol/l (t
30
–t
0
).
Stress Paradigm
Upon arrival, participants gave written informed consent and
were allowed to get acquainted to the test hardware outside the
scanner. Afterwards, they spend 30 min in a quiet room to
acclimatize. Brain function during social evaluative stress
was studied using fMRI and the Montreal Imaging Stress
Task (MIST) (Dedovic et al, 2005) as detailed by us pre-
viously (Lederbogen et al, 2011). Briefly, the task consisted of
three different experimental conditions that were repeated
twice in each of the three scan runs, lasting 7 min each. In the
stress condition, participants were asked to solve cognitively
demanding arithmetic problems displayed on a screen.
Time pressure was induced by a countdown timer that was
continuously adapted to the subjects’performance levels
to ensure error rates in the range of 60–75%. After each
incorrect response, a negative visual feedback was displayed.
Social-evaluative threat was imposed through negative verbal
feedback given by the experimenters between experimental
runs. In the control condition, the arithmetic problems were
presented and solved without time pressure and any feedback.
In the resting condition, subjects observed a blank screen. A
T1-weighted structural MRI scan was acquired immediately
before the start of the stress test.
Saliva cortisol was measured at seven time points throughout
the experimental session (after rest (Cort1), before entering
the scanner (Cort2), after the anatomical scan (Cort3), after
MIST runs 1 to 3 (Cort4 to 6), after leaving the scanner
(Cort7)). The increase in cortisol was calculated as the
difference between cortisol samples taken before entering the
scanner (Cort2) and at the group maximum cortisol level at
the end of the scanning session (Cort7, see Table 1). We used
this measure of cortisol reactivity instead of the area under
the curve because cortisol samples during MRI scanning
were not fully available for two participants. Heart rate was
measured continuously during the course of the stress task
using an MR-compatible fingertip pulse oximeter. Subjective
responses to stress were quantified prior to and after stress
induction using an 11-point rating analogue scale ranging
from 0 (absence of any subjective stress) to 10 (maximum
subjective stress intensity).
Comparability of Baseline Cortisol Values
As CAR assessment and MIST exposure took place on
separate days, we tested whether basal saliva cortisol levels
on the day of the CAR assessment and the day of the MIST
exposure were comparable. To this end, we used the cortisol
data measured on the day of the CAR assessment to predict
the cortisol level at the time of Cort2 (pre-scanning baseline)
acquisition on the day of the MIST exposure and tested
whether there was a significant correlation between the
predicted and the measured cortisol level at Cort2. In more
detail, we fitted a quadratic function to the cortisol data
measured on the day of the CAR assessment with time of
measurement (time of t
0
,t
8
,t
14
, acquisition) as predictor and
cortisol level at these time points as dependent variable. The
cortisol value measured 30 min after awakening (t
30
) was
excluded from this analysis because the CAR reflects an
increase in cortisol that is superimposed on the overall
diurnal pattern of cortisol secretion (Wilhelm et al, 2007).
The parameters from this function were then used to predict
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A Boehringer et al
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the cortisol level at the time of Cort2 assessment on the day
of the MIST. We found a significant correlation between the
predicted cortisol level at the time of Cort2 acquisition and
the actually measured Cort2 cortisol level on the day of the
MIST (r =0.42, po0.05), indicating that the baseline values
on both days of measurement (CAR assessment vs MIST
exposure) were indeed comparable.
fMRI Data Acquisition and Analysis
Blood-oxygen-level-dependent fMRI was performed on a 3.0
Tesla Siemens Trio scanner using a gradient-echo echo-
planar-imaging sequence with the following specifications:
repetition time =2000 ms, echo time =30 ms, flip angle =
80°, 64 × 64 matrix, 192 mm field of view, 32 axial slices,
4 mm slice thickness, 1 mm gap. In addition, we used a 3D
magnetization-prepared rapid gradient echo sequence
(TR =1300 ms; TE =3.93 ms) to acquire a T1-weighted scan
of the entire brain (alpha =10°; sagittal orientation; spatial
resolution =1 × 1 × 1.5 mm).
Functional image processing followed previously published
procedures using standard processing routines in SPM8
(Wellcome Department of Imaging Neuroscience Group,
London, UK, http://www.fil.ion.icl.ac.uk/spm). Briefly, all
images were realigned, spatially normalized to the Montreal
Neurologic Institute (MNI) template, and smoothed with a
9 mm full-width at half-maximum Gaussian kernel. For
functional connectivity analyses, data preprocessing included
slice-time correction. For functional activation analysis,
separate general linear models were defined for each subject
by modeling the alternating fMRI task conditions by
convolving a box-car reference vector with the canonical
hemodynamic response function implemented in SPM8. To
account for residual motion artifacts, the convolved motion
regressors from the realignment step were included as
nuisance covariates. At the model estimation stage, the data
were high-pass filtered with a cutoff of 128 s. Contrast
images were calculated for each subject to identify brain
regions with greater activation during the stress conditions
relative to the control conditions (stress4control).
These individual first-level contrast images were subse-
quently subjected to group-level statistical inference using
multiple regression models with CAR values as covariate of
interest, and age, sex, and time of awakening as nuisance
covariates. To reflect our a priori hypothesis, significance
level for the activation analysis was set to Po0.05 family-
wise error (FWE) corrected for multiple comparisons over
an a priori defined anatomical mask of the pACC derived
from the Harvard Oxford Atlas as available in FSL (http://fsl.
fmrib.ox.ac.uk/fsl/fslwiki/) that has previously been used
by our group (Lederbogen et al, 2011). We used the FWE
correction implemented in SPM8, which is based on
Gaussian Random fields theory (Worsley et al, 1996). Our
ACC mask covered the pACC (including BA 24 a-c, BA 25,
BA 32, and BA 33, see Supplementary Figure S2) as defined
by Bush et al, (2000).
Functional Connectivity Analysis
Seed-based functional connectivity between the pACC and
the hypothalamus was assessed using the software package
Lipsia 2.0 (www.cbs.mpg.de/institute/software/lipsia/index.
html). We focused our analysis on low-frequency fluctua-
tions following previously published procedures (Lohmann
et al, 2010). First, the preprocessed functional images were
band-pass filtered between 0.1 and 0.01 Hz and baseline
drifts were removed. Moreover, despiking as implemented in
Lipsia 2.0 was used to remove values in a voxel’s blood-
oxygen-level-dependent time series above or below 4
standard deviations of the mean value of this voxel’s time
series. Next, variance related to the stress task, and several
nuisance variables (blood-oxygen-level-dependent time ser-
ies for white matter, cerebrospinal fluid, and six motion
parameters) was removed from every voxel’s time series
using a general linear model. Blood-oxygen-level-dependent
time series for white matter and CSF were derived from
4 mm spherical masks centered in the deep white matter and
the lateral ventricles in MNI152 space. Next, the mean
residual time series from voxels within a 4 mm spherical
mask around the peak of the CAR effect in the pACC was
extracted and Pearson’s correlation coefficients of the
averaged time course with all other voxels in the brain were
calculated. The resulting correlations were normalized using
Fisher’s r-to-z transformation and subjected to a second level
Table 1 Saliva Cortisol (Cort1 to Cort7), Heart Rate Systolic and Diastolic Blood Pressure (BP), and Perceived Stress (Visual Analogue Scale)
at Seven Time Points throughout the Experimental Session as well as in Response to the Stress Task
Stress task (MIST)
Rest Pre MRI After
anatomical scan
After MIST
Run1
After MIST
Run2
After MIST
Run3
Post MRI Stress reactivity
and CAR
Cort1 to Cort7 (nmol/l) 9.0 ±4.1 6.6 ±3.1 7.8 ±5.3 8.2 ±5.5 9.8 ±7.5 12.0 ±8.7 14.8 ±11.2 7.7 ±10.6
Heart rate (bpm) 61.2 ±5.6 66.8 ±5.2 66.9 ±10.2 82.1 ±20.2 83.6 ±21.4 83.8 ±19.6 72.5 ±9.6 15.9 ±19.0
Systolic BP (mm Hg) 123.7 ±13.5 135.4 ±15.8 11.1 ±10.4
Diastolic BP (mm Hg) 79.2 ±7.9 87.8 ±8.5 8.4 ±8.6
Perceived stress 2.5 ±1.2 7.2 ±1.7 4.7 ±2.0
CAR (nmol/l) 7.2 ±8.3
The Cortisol awakening response (CAR) was measured on a separate day as the increase in saliva cortisol within the first 30 min after awakening, Cort1 to Cort7:
Cort1 =after the initial resting period, Cort2 =before entering the scanner, Cort3 =after the anatomical MRI scan, Cort4 to Cort6 =after MIST runs 1 to 3,
Cort7 =after leaving the MRI scanner, MIST =Montreal Imaging Stress Task, MRI =Magnetic resonance imaging.
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analysis as described above. Significance was measured at
Po0.05 FWE-corrected for multiple comparisons in a
probabilistic mask covering bilateral hypothalamus taken
from the WFU pickatlas (Maldjian et al, 2003).
Voxel-Based Morphometry (VBM)
Structural MRI data were preprocessed using SPM8 and the
VBM8 toolbox (http://dbm.neuro.uni-jena.de/vbm). Images
were corrected for bias-field inhomogeneities and tissue-
classified into grey matter (GM), white matter, and cerebro-
spinal fluid (see the VBM8 user guide for a more detailed
description). Resulting GM segments were normalized using
DARTEL, and multiplied by the nonlinear components
derived from the normalization matrix to preserve local GM
values, while accounting for individual differences in brain
size. Finally, spatial smoothing with a Gaussian kernel of
10 mm full-width at half-maximum was applied. Prepro-
cessed GM images were then subjected to a multiple
regression analysis with CAR values as covariate of interest,
and age and sex as nuisance covariates. Significance was
defined as detailed for the fMRI activation analysis.
Control for Effects of Participants’Sex and the
Temporal Difference between the CAR Assessment and
theTimeoftheMISTExposure
In the present study, data on history of menses and history of
oral contraceptives use were not available for our female
participants. As previous work showed that female meno-
pausal status can affect individual differences in the CAR
(Pruessner et al, 1997), we tested whether participants’sex
affected our imaging results. To this end, we extracted contrast
estimates and GM volume (GMV) scores for all participants
from peak voxels identified in our imaging analyses and used
these as dependent variables in a general linear model including
sex, the CAR, and the interaction term between these
variables as parameters of interest. Moreover, as described
above, we statistically controlled for sex effects in all voxel-
wise imaging analyses.
As the temporal distance between the day of the CAR
assessment and the day of the MIST exposure may have
affected our results, we included the temporal difference
between both days as an additional nuisance variable in the
general linear model described above and re-run all imaging
analyses. Notably, none of the reported neuroimaging results
was affected by including the temporal distance as a nuisance
variable into the models.
RESULTS
Response to Stress Induction
Across all participants, performance of the fMRI experiment
resulted in significant increases in salivary cortisol (Po0.001),
heart rate (Po0.001), systolic and diastolic blood pressure
(Po0.001), and subjective feelings of stress (visual analogue
scale, Po0.001), indicating that stress was successfully induced
in the scanner. Mean endocrine, cardiovascular, and subjective
stress responses as well as mean magnitude of the CAR are
presented in Table 1. Individual differences in the CAR were
neither significantly associated with stress-induced cortisol
(r =0.10, P=0.63) nor the group peak cortisol level (Cort7,
r=0.19, P=0.37).
At the neural level, corresponding to our previously
reported work (Lederbogen et al, 2011), the stress condition
resulted in significant differential activation in a distributed
network of brain regions including core regions of the
extended limbic circuitry such as pACC, hippocampus,
amygdala, and hypothalamus (all P-valueso0.01, FWE
corrected for the whole brain, see Supplementary Figure S1).
CAR and Brain Function
Investigating associations between the CAR (defined as the
increase in saliva cortisol within the first 30 min after
awakening, t
30
–t
0
) and stress-related brain activity, we found
a significant inverse relationship between the CAR and
stress-related activity in a pregenually located area within our
pACC region of interest (MNI 0 30 0, T
1,20
=4.02, po0.05,
FWE-corrected for bilateral pACC; Figure 1, see Table 2 for
results of an exploratory whole-brain analysis). Overall, brain
activity in this pACC region increased during stress (MNI 0
33 3, T
1,20
=4,81, p=0.01, FWE corrected for bilateral
pACC). Participants’sex did not affect this relationship (sex
by CAR interaction: F
1,21
=0.24, P=0.63) We reasoned that,
if the pACC region showing the association with the CAR
was directly involved in neural control of the HPA axis, then
it should functionally interact with the hypothalamus, the
final controller of HPA axis activity. Moreover, individual
differences in the magnitude of the CAR should be associated
with the strength of these interactions. We measured
functional connectivity between the pACC and the hypotha-
lamus to test these hypotheses and found that both regions
were significantly positively functionally connected during
stress (MNI −60−9, T
1,20
=11.42, Po0.001, FWE-corrected
for hypothalamus). Moreover, individual differences in the
CAR were inversely associated with the strength of func-
tional connectivity between the pregenual pACC and the
hypothalamus (MNI −6−3−12, T
1,20
=4.39, Po0.05, FWE-
corrected for hypothalamus, Figure 2). Again, participants’
sex did not affect this relationship (sex by CAR interaction:
F
1,21
=0.98, P=0.33)
In order to investigate the specificity of our functional
imaging finding for the CAR, we tested whether individual
differences in stress-induced cortisol predicted stress-related
brain activity in the pACC. At a liberal uncorrected level of
po0.01, stress-induced cortisol predicted stress-related brain
Figure 1 CAR and stress-related activity. Individual differences in the
CAR (assessed as the increase in cortisol within the first 30 min after
awakening, t
30
–t
0
) predict stress-related brain activity in a pregenual area of
the pACC (T
1,21
=4.04, po0.05, FWE-corrected for bilateral pACC, shown
at Po0.005 uncorrected for display purposes).
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activity in the anterior pACC (MNI 6 54 0, T
df =21
=2.65).
This effect did not survive correction for multiple testing
(p40.30 FWE-corrected for bilateral pACC) and did not
overlap with the pACC area showing the association with
the CAR.
CAR and Brain Structure
We conducted a VBM analysis to identify associations
between the CAR and GMV in the pACC. Individual
differences in the CAR showed a significant positive
association with local GMV in the dorsal anterior pACC
(MNI 8 51 13, T
1,20
=4.10, po0.05 FWE-corrected for
bilateral pACC, Figure 3). This effect extended into the
adjacent medial prefrontal cortex and did not overlap with
the pregenual pACC area identified in our functional
imaging analysis. The relationship between pACC GMV
and the CAR was not affected by participants’sex (sex by
CAR interaction: F
1,21
=0.07, P=0.79). No voxels in the
pACC showed a significant association with stress-induced
cortisol even at a liberal uncorrected threshold of po0.01.
In order to test whether there was a relationship between
GMV in the dorsal anterior pACC and stress-related activity
in the pregenual pACC, we extracted GMV and stress-related
activity from peak voxels identified in our VBM and brain
activity analyses for all subjects and calculated the partial
correlation between these extracted values controlling for age
and sex. We found a significant negative relationship between
GMV in the dorsal anterior pACC and stress-related activity
in the pregenual pACC (partial r =−0.52, po0.05).
DISCUSSION
The current study provides evidence for a link between
pACC structure, function, and connectivity with the CAR
that supports a role of this brain region in the neural control
of the HPA axis in humans.
Consistent with the established role of the ACC in HPA
axis inhibition, we found lower stress-related brain activity in
the pACC in individuals with a higher CAR. One mechanism
through which the ACC inhibits HPA axis activity is
glucocorticoid receptor-mediated feedback control (Ahima
and Harlan, 1990; Diorio et al, 1993; Boyle et al, 2005). As
cortisol increased in response to the present stress task, there
is a possibility that the inverse association between the CAR
and pACC activity is affected by such feedback-related
glucocorticoid receptor signaling in response to stress-
induced cortisol. However, pACC activity was not associated
with the task-induced cortisol increase and appears therefore
not to reflect neural processes directly involved in gluco-
corticoid receptor-mediated feedback signaling.
In line with previous work (Schmidt-Reinwald et al, 1999),
individual differences in the CAR were not associated with
stress-induced cortisol, suggesting that the psychosocial
stress-induced increase in HPA axis activity and the CAR
represent different aspects of HPA axis functioning. The
CAR is a measure of basal HPA axis regulation and might as
such more strongly be affected by brain mechanism involved
in the tonic control of HPA axis activity than the stress-
induced change in saliva cortisol. The brain mineralocorti-
coid receptor binds cortisol with high affinity and is essential
for the maintenance of the basal circadian rhythm and tonic
regulation of the HPA axis (Joels et al, 2008). Mineralo-
corticoid receptors are expressed with high density in the
hippocampus and, to a lesser extent, in the cerebral cortex
including the medial prefrontal cortex (Patel et al, 2000). It
has recently been shown that genetic variation in the
mineralocorticoid receptor gene affects the magnitude of
the CAR after ingestion of a low dose of dexamethasone, a
Table 2 Brain Regions Showing Associations Between
Stress-Related Activity and Individual Differences in the CAR
(voxel Po0.001 uncorrected)
MNI coordinate (mm)
Region Peak T value (df =21) xyz
Insula right 6.14 45 −12 0
Insula left 5.48 −51 −90
Middle temporal gyrus 4.49 39 −3−39
Post central gyrus 4.38 −51 −18 45
pACC 4.04 0 30 0
Inferior frontal gyrus 3.96 60 12 21
Figure 2 CAR and pACC functional connectivity (FC). FC between the
pregenual pACC (seed region indicated as blue circle) and the
hypothalamus was associated with individual differences in the CAR
(assessed as the increase in cortisol within the first 30 min after awakening,
t
30
–t
0
,T
1,21
=4.39, Po0.05, FWE-corrected for hypothalamus, shown at
Po0.005 uncorrected for display purposes).
Figure 3 CAR and grey matter volume (GMV). GMV in the dorsal
anterior pACC is associated with individual differences in the CAR (assessed
as the increase in cortisol within the first 30 min after awakening, t
30
–t
0
,
T
1,21
=4.27, po0.05 FWE-corrected for bilateral pACC, shown at
Po0.001 uncorrected for display purposes). This effect extends into the
adjacent medial prefrontal cortex and is located anterior to the pregenual
pACC region identified in our functional imaging analysis (see Figure 1).
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synthetic glucocorticoid, supporting a role of the miner-
alocorticoid receptor in the CAR (van Leeuwen et al, 2010).
On the basis of these data, we speculate that individual
differences in mineralocorticoid receptor signaling in the
pACC might play a role in the CAR. Future studies are
needed to test this hypothesis directly.
Consistent with our brain activity findings, the CAR was
positively associated with GMV in the dorsal anterior pACC.
This structural phenotype is in good agreement with the
previous literature linking the CAR to psychosocial risk.
Childhood trauma, a major environmental risk factor for
important mental illnesses, has been associated with a lower
CAR and reduced GMV in the ACC in most (Cohen et al,
2006; Heim et al, 2009; Mangold et al, 2010) but not all
studies (Lu et al, 2013). The latter study also reported a
negative association between cingulate gyrus volume and the
CAR. However, this effect was restricted to the middle
cingulate gyrus, which was not in the focus of the present
study. A lower CAR and reduced pACC GMV have also been
found in patients with PTSD (Karl et al, 2006; Wessa et al,
2006). The link to major depression is less clear as it has
typically been found associated with reduced pACC GMV
(Bora et al, 2012) but linked to an increase in the CAR in
most (Vreeburg et al, 2009; Lamers et al, 2013) but not all
studies (Stetler and Miller, 2005). However, subclinical
depressive symptoms in a community sample have been
linked to a lower CAR (Dedovic et al, 2010). Given that we
studied healthy participants, who were carefully screened for
depression, the directionality of our structural imaging data
is in line with this latter finding. The present finding of a
relationship between pACC GMV and the CAR in healthy
individuals support a speculation that alterations in pACC
GM structure may mediate changes in HPA axis function,
which may in turn contribute to the development of
psychiatric disorders, such as MDD, schizophrenia, and
PTSD in traumatized individuals. Although the present study
cannot address whether changes in pACC GMV are primary
or secondary to alterations in HPA axis function, it appears
plausible that stress-induced HPA axis alterations could
cause changes in pACC GMV as the ACC is a well-known
target of glucocorticoid action.
The negative relationship between GMV in the dorsal
anterior pACC and stress-related activity in the pregenual
pACC suggest that both pACC regions participate in a neural
system involved in control of the CAR. Given that stress-
related activity in the dorsal anterior pACC was not
associated with the CAR, higher GMV in this pACC region
could reflect a compensatory increase in GMV associated
with lower reactivity of the pregenual pACC to stress.
However, future studies, including longitudinal approaches,
are necessary to examine the relationship between pACC
GMV and stress-related activity with respect to the CAR in
more detail.
Owing to its dense connections to downstream limbic
structures including the hypothalamus (Etkin et al, 2011),
and supported by functional imaging and electrophysiologi-
cal studies, the pACC is considered to stand at the top of the
response hierarchy that regulates stress-related HPA axis
activation (Rosenkranz et al, 2003; Pezawas et al, 2005;
Ulrich-Lai and Herman, 2009). Moreover, individual differ-
ences in fiber bundle asymmetry in the cingulate cortex have
been linked to the CAR in a recent MRI study using diffusion
tensor imaging (Madsen et al, 2012). Our finding of lower
functional connectivity between the pACC and the hypothal-
amus in individuals with a higher CAR indicates that
individual differences in pACC–subcortical interactions
also play a role in the CAR and thus provides a neural
mechanism through which altered pACC functioning may
affect the CAR.
Here, we focused our analyses on the pACC, a brain region
known to be involved in neural control of the HPA axis and
the pathophysiology of various stress-related mental dis-
orders. An exploratory whole-brain analysis did not reveal
associations between stress-related brain activity and the
CAR in other known regulators of the HPA axis, importantly
the amygdala and the hippocampus, even at an uncorrected
level. Although this finding does not exclude a role of the
amygdala and the hippocampus in the CAR, it suggests that
pACC networks involved in psychosocial stress play a
specific role in this measure of HPA axis reactivity.
This first study of the neural regulation of the CAR has
several limitations. Owing to the correlational and cross-
sectional nature of our data, we cannot infer directionality or
causality of our links between pACC structure and function
and HPA axis reactivity. However, as we have discussed,
our observations fit with preclinical evidence and clinical
observation in at-risk and patient populations. Secondly, as
our participants were healthy, any implications related to
psychiatric risk are suggested by previous literature, not the
data presented here. Thirdly, although the CAR has been
shown to be a longitudinally stable readout of HPA axis
activity (Wust et al, 2000b), it is affected by several situa-
tional factors, such as chronic stress, weekday to weekend
differences, and failure of adherence to the sampling protocol
which affect day-to-day variability. In addition, the degree of
intra-individual variability of the CAR has been reported to
be associated with subject age and sex (Almeida et al, 2009)
and seasonality, ie, the tendency to show seasonal variation
in mood, is associated with the CAR (Thorn et al, 2009). As
we measured the CAR with a substantial temporal distance
from the stress test, individual differences in seasonality may
have affected our results. Here, we aimed at minimizing
these state effects on our imaging results by conducting the
CAR assessment as well as the stress test on a regular
weekday and instructing the participants to adhere closely to
the sampling protocol and to report the exact time point of
awakening and saliva sampling. Moreover, we statistically
controlled for age and sex in all imaging analyses and tested
whether controlling for the temporal distance between the
CAR assessment and the MIST exposure affected our findings,
which was not the case. Nevertheless, we cannot exclude the
possibility that some of the inter-individual variability in the
CAR in the present sample is explained by such situational
factors that may have affected our imaging results. As it has
been reported that aggregation of CAR measurements from
different days increases its independence from situational
factors (Almeida et al, 2009) and thus reduces intra-
individual variability, we recommend this strategy for future
studies. Fourth, we measured saliva cortisol at two time
points after awakening only. Measuring cortisol more often
would allow a more fine-grained examination of the
relationship between morning HPA axis activity and stress-
related brain activity as well as brain structure. Finally, it
must be borne in mind that functional and structural MRI
Neural correlates of the cortisol awakening response
A Boehringer et al
6
Neuropsychopharmacology

parameters only indirectly measure neuronal function and
integrity.
With these caveats, our data provide initial evidence for a
role of a stress-related pACC-hypothalamic circuit in neural
control of the CAR in humans. Given that the pACC has
been implicated strongly in the pathophysiology of major
psychiatric disorders including MDD and PTSD, as well as
environmental risk for mental illness, these findings may aid
future research on the pathophysiology of stress-related
mental disorders.
FUNDING AND DISCLOSURE
The research leading to these results has received funding
from the European Community’s Seventh Framework
Program under grant agreement No. HEALTH-F2-2010-
241909 (Project EU-GEI), German Research Foundation
(Deutsche Forschungsgemeinschaft SFB 636-B7) and Federal
Ministry of Education and Research (MooDS) to A.M.L.
EU-GEI is an acronym for the project ‘‘European network
of National Schizophrenia Networks Studying Gene–
Environment Interactions’’. Dr Meyer-Lindenberg has
received consultant fees and travel expenses from Alexza
Pharmaceuticals, AstraZeneca, Bristol-Myers Squibb,
Defined Health, Decision Resources, Desitin Arzneimittel,
Elsevier, F. Hoffmann–La Roche, Gerson Lehrman Group,
Grupo Ferrer, Les Laboratoires Servier, Lilly Deutschland,
Lundbeck Foundation, Outcome Sciences, Outcome Europe,
PriceSpective, and Roche Pharma and has received speaker’s
fees from Abbott, AstraZeneca, BASF, Bristol-Myers Squibb,
GlaxoSmithKline, Janssen- Cilag, Lundbeck, Pfizer Pharma,
and Servier Deutschland. No other disclosures were reported.
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Supplementary Information accompanies the paper on the Neuropsychopharmacology website (http://www.nature.com/npp)
Neural correlates of the cortisol awakening response
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