Prefrontal serotonin transporter availability is positively associated with the cortisol awakening response

European Neuropsychopharmacology (2013) 23, 285–294

www.elsevier.com/locate/euroneuro

Prefrontal serotonin transporter availability is positively associated with the cortisol awakening response Vibe Gedsoe Frokjaera,b,n, David Erritzoea,b, Klaus K¨ ahler Holsta,g, Peter Steen Jensena,b, Peter Mondrup Rasmussena,e,f, Patrick MacDonald Fishera,b, William Baare a,h, Kathrine Skak Madsena,h, Jacob Madsenc, Claus Svarera,b, Gitte Moos Knudsena,b,d a

Center for Integrated Molecular Brain Imaging, DK-2100 Copenhagen, Denmark Neurobiology Research Unit, DK-2100 Copenhagen, Denmark c PET and Cyclotron Unit, Rigshospitalet, Denmark d Health Science Faculty, University of Copenhagen, Denmark e DTU Informatics, Technical University of Denmark, Denmark f The Danish National Research Foundation’s Center for Functionally Integrative Neuroscience, Aarhus University Hospital, Denmark g Department of Biostatistics, University of Copenhagen, Denmark h Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital, Hvidovre, Denmark b

Received 8 December 2011; received in revised form 10 April 2012; accepted 29 May 2012

KEYWORDS

Abstract

5-HTT; 5-HTTLPR; PET; DASB; Glucocorticoid; Stress

Stress sensitivity and serotonergic neurotransmission interact, e.g. individuals carrying the lowexpressing variants (S and LG) of the 5-HTTLPR promoter polymorphism of the serotonin transporter (SERT) gene are at higher risk for developing mood disorders when exposed to severe stress and display higher cortisol responses when exposed to psychosocial stressors relative to high expressing 5-HTTLPR variants. However, it is not clear how the relation between SERT and cortisol output is reflected in the adult brain. We investigated the relation between cortisol response to awakening (CAR) and SERT binding in brain regions considered relevant to modify the cortisol awakening response. Methods: thirty-two healthy volunteers underwent in vivo SERT imaging with [11C]DASBPositron Emission Tomography (PET), genotyping, and performed home-sampling of saliva to assess CAR. Results: CAR, defined as the area under curve with respect to increase from baseline, was positively coupled to prefrontal SERT binding (p=0.02), independent of adjustment for 5-HTTLPR genotype. Although S- and LG-allele carriers tended to show a larger CAR (p=0.07) than LA homozygous, 5-HTTLPR genotype did not modify the coupling between CAR and prefrontal SERT binding as tested by an interaction analysis (genotype  CAR). Conclusion: prefrontal SERT binding is

n

Corresponding author at: Neurobiology Research Unit 9201, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen, Denmark. Tel.: +45 35456712; fax: +45 35456713. E-mail address: [email protected] (V.G. Frokjaer). 0924-977X/$ - see front matter & 2012 Elsevier B.V. and ECNP. All rights reserved. http://dx.doi.org/10.1016/j.euroneuro.2012.05.013

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V.G. Frokjaer et al. positively associated with cortisol response to awakening. We speculate that in mentally healthy individuals prefrontal serotonergic neurotransmission may exert an inhibitory control on the cortisol awakening response. & 2012 Elsevier B.V. and ECNP. All rights reserved.

1.

Introduction

Stress responses and serotonergic neurotransmission interact, at least partially genetically (Caspi et al., 2003; Karg et al., 2011). In humans the SERT (5-HTT)-linked polymorphic region (5-HTTLPR) has two frequent alleles, designated long (L), and short (S). The S-allele is associated with lower SERT expression in human cell lines. A functional single nucleotide variant exists within L, designated LA and LG (Nakamura et al., 2000); LA is associated with high levels of in vitro 5-HTT expression, whereas LG is low expressing and more similar to S. Adult LA/LA carriers display slightly higher cerebral SERT binding (Willeit and Praschak-Rieder, 2010). Individuals carrying low expressing variants of the 5-HTTLPR are at higher risk for developing major depression when exposed to severe stress (Caspi et al., 2003; Karg et al., 2011) and show an increased cortisol response to psychosocial stressors (Alexander et al., 2009; Chen et al., 2009; Gotlib et al., 2008; Mueller et al., 2010; Way and Taylor, 2010). This provides a plausible mechanism by which environmental stress in combination with inherited vulnerability may lead to mood disorders since neurobiological consequences of stress (Holsboer, 2001; Ising et al., 2005; Manji et al., 2001; Mannie et al., 2007), play a role in the pathophysiology. However, it is not clear how low-expressing as compared to high-expressing variants of the 5-HTTLPR lead to increased cortisol responses. Activation of the hypothalamic-pituitary-adrenal (HPA)-axis, e.g. in response to a stressor, leads to cortisol secretion. Basal secretion of cortisol follows a diurnal cycle with higher values in the early morning and lower values in the evening. The cortisol awakening response (CAR) is a distinct feature of HPA-axis activity (Fries et al., 2009; Pruessner et al., 1997; Wilhelm et al., 2007). Awakening in the morning provokes a profound 50–75% rise in plasma cortisol that peaks at around 30 min after awakening and returns to baseline levels within about 60 min. CAR is seen in 75% of healthy individuals under home-sampling conditions (Wust et al., 2000b). Twin studies report that CAR is moderately heritable (heritability estimate=0.48) (Wust et al., 2000a), in contrast to other diurnal measures of HPA-axis activity (Kupper et al., 2005), further, it seems associated with 5-HTTLPR genotype status in a gender specific manner (Wust et al., 2009). CAR, as defined by the area under the curve with respect to increase from awakening level, represents both the rise provoked by awakening and the subsequent restoration to baseline. Therefore, it may also index the regulatory capacity of the system. HPA-axis output in response to psychosocial stressors and to awakening appear to be correlated (Chen et al., 2009; Gotlib et al., 2008). In contrast to responses to more extreme stress tests, CAR reflects the HPA-axis output in a basal everyday condition that may be particularly relevant when studying the physiology of the system. The HPA-axis output is regulated by the limbic system and associated areas and includes excitatory effects from

amygdala and inhibitory effects from hippocampus and prefrontal cortex (Pruessner et al., 2010). The neural output of these brain regions are integrated in the paraventricular nucleus of hypothalamus that regulates corticotropin release. Both hippocampus and prefrontal cortex express glucocorticoid and mineralocorticoid receptors and mediate negative feedback from circulating cortisol (Herman et al., 2005). Serotonin is involved in stress adaptation and regulates autonomic and endocrine responses to stress. Serotonergic neurotransmission can both facilitate and inhibit the HPAaxis (Lowry, 2002). Animal studies have shown a profound influence of both acute and chronic stress on serotonin release and reuptake, extracellular serotonin levels, and pre- and post-synaptic serotonin receptors in limbic areas and in raphe nuclei (Chaouloff, 2000). In general, stress enhances serotonin output, and in turn, the serotonin system influences the secretion of corticosteroids, e.g., stimulation of the 5-HT1A receptor massively increases corticosteroid levels in both animals and humans (Lanfumey et al., 2008). Also, 5-HT1A autoreceptors desensitize in response to stress, hippocampal 5-HT2A receptors increase in response to 7 day high levels of corticosteroid (Trajkovska et al., 2009), and prefrontal 5-HT2C receptors may be involved in negative feedback. Forster et al. (2006) have shown that increased serotonin levels in the medial prefrontal cortex are associated with decreased behavioral responses to fear and stress (Forster et al., 2006). Additionally, this association appears to be a delayed consequence of corticotropin releasing factor signaling at the level of the raphe nuclei further linking serotonergic neurotransmission and HPA-axis function (Forster et al., 2008). This suggests that serotonergic tone in the medial prefrontal cortex modulates behavioral stress responses by inhibitory actions. Nevertheless, the role of serotonin in regulating HPA-axis activity is far from clear and characterization of ways by which serotonergic neurotransmission may modulate the human HPA-axis activity is of particular interest in understanding the role of serotonin in adaptation (or maladaption) to stress and in the pathophysiology of e.g. mood disorders. Here, we studied healthy adults to determine if cerebral SERT availability, as evaluated by [11C]DASB-Positron Emission Tomography (PET), is associated with HPA-axis output in terms of CAR and whether this association is modified by 5-HTTLPR genotype. We hypothesized that SERT binding in cortical brain regions relevant for HPA-axis regulation correlate with CAR in healthy volunteers.

2.

Experimental procedures

Thirty-two healthy volunteers (mean age 35720, range 20–82 years, 7 women) underwent SERT imaging with [11C]DASB-PET and performed saliva home-sampling for determination of CAR. None of

SERT and cortisol awakening response the subjects had a personal history of present or prior neurological or psychiatric disorders or took psychoactive drugs, hormonal replacement, beta-adrenergic blocking agents, or drugs of abuse as evaluated by interview. Written informed consent was obtained from all participants. Personality and perceived stress was characterized by Danish versions of self-report NEO-PI-R questionnaires (Skovdahl-Hansen, 2004), and Cohen’s perceived stress scale (Cohen et al., 1983). Genotyping of the short (S) and long (L) 5-HTTLPR variants, and subtyping of single nucleotide variants within L (rs25531 LA/LG) was performed as described earlier (Kalbitzer et al., 2010). The participants were included as healthy volunteers in 3 clinical projects (Erritzoe et al., 2011; Marner et al., 2010; Rasmussen et al., 2010). PET imaging. SERT binding was imaged with [11C]DASB PET based on 90 min dynamic acquisition starting immediately after bolus injection of 6.471.6 MBq [11C]DASB per kg body weight. PET scans were performed with an 18-ring GE-Advance scanner (General Electric, Milwaukee, WI, USA), operating in 3D acquisition mode, producing 35 image slices with an interslice distance of 4.25 mm. The outcome parameter of the [11C]DASB binding is the ratio between specific binding and non-displaceable binding of the tracer, BPND. We used a modified reference tissue model designed specifically for quantification of [11C]DASB (MRTM/MRTM2) by Ichise et al. (2003) implemented in PMOD, software version 2.9, build 2 (PMOD Technologies), using cerebellum without vermis, as a reference region. Movement correction, coregistration of [11C]DASB mean image to the high-resolution T1 weighted magnetic resonance image (MRI) by AIR (Woods et al., 1992), and further details on [11C]DASB imaging and quantification are described in Frokjaer et al. (2009). Partial volume correction of PET-data was based on tissue classification and performed according to Quarantelli et al. (2004) using the Mueller-Gartner method. The white matter value was extracted as the mean voxel value from a predominantly white matter VOI (centrum semiovale) in the uncorrected PET image. MR imaging. MRI was acquired on a Siemens Magnetom Trio 3T MR scanner (Invivo, FL, USA). Two slightly different high-resolution 3D T1-weighted, sagittal, magnetization prepared rapid gradient echo (MPRAGE) scans were acquired in 8 and 24 subjects, respectively. MPRAGE1: echo time (TE)/repetition time (TR)/inversion time (TI) =3.93/1540/800 ms; slice resolution (SC)=75%; bandwidth (BW)=130 (Hz/Px); Echo spacing (ES)=9.8 ms. MPRAGE2: TE/TR/ TI=3.04/1550/800 ms; SC=100%; BW=170 (Hz/Px); ES=7.7 ms. Both scans had: flip angle=91, FOV =256 mm, matrix=256  256, 1  1  1 mm3 voxels, 192 slices. Additionally, 8 subjects underwent a 2D T2weighted, axial, Turbo Spin Echo (TSE) scan (TE1/TE2/TR=17/100/ 9000 ms; FOV =220 mm, matrix=256  256; GRAPPA: acceleration factor=2; reference lines= 30; 0.9  0.9  3 mm3 voxels; 50 slices) and 24 subjects underwent a 3D T2-weighted, sagittal, TSE scan (TE/TR)=354/3000 ms; FOV=282  216; matrix=256  196; 192 sagittal slices; 1.1  1.1  1.1 mm3 voxels. Both T1 and T2 images were corrected for spatial distortions due to non-linearity in the gradient system of the scanner software (Jovicich et al., 2006). Subsequently, non-uniformity correction was performed with two iterations of the N3 program (Sled et al., 1998). The resulting T1 images were intensity normalized to a mean value of 1000. T1-weighted MR images were segmented into gray matter, white matter, and cerebrospinal fluid using SPM2 (Welcome Department of Cognitive Neurology, University College London, UK) and the Hidden Markov Random Field model as implemented in the SPM2 VBM toolbox (http://dbm.neuro.uni-jena.de/vbm/). Brain masks based on the corrected T2-weighted MRI were used to exclude extra-cer ebral tissue. Volumes of interest (VOIs). Based on the method by Svarer et al. (2005) VOIs were automatically delineated on each individual’s MRI in a strictly user-independent fashion. The VOIs included prefrontal cortex (computed by pooling orbito-, superior-, and medial- and

287 inferior-frontal cortex), and anterior cingulate. Although, hippocampus, hypothalamus, and amygdala could be of potential interest, binding estimates in these (for PET purposes) smaller structures are noisy, therefore, they were not included in the VOI analyses. Cerebellum (excluding vermis) served as a reference region. Midbrain was delineated for follow-up analysis purpose. Time-activity curves were extracted from gray matter voxels only, except for midbrain, where gray and white matter cannot be separated reliably. Parametric images representing BPND for each voxel were calculated for all participants from the dynamic PET images using the PXMOD tool in the PMOD software. According to Ichise et al. (2003) we applied a threshold of 0.3 to R1 (defined as the relative tracer delivery to a voxel relative to that in cerebellum) to exclude noisy voxels. Additionally, we constrained the BPND outcome to a physiologically plausible range between 0 and 10. The resulting parametric images were filtered with a 3  3  3 voxel median filter in their native space.

2.1.

Saliva cortisol

HPA-axis activity was characterized by CAR (Pruessner et al., 1997) 21736 (mean7std); 7.5 (median); [1–149] days from PET scan (3 extremes: 90, 142 and 149 day). CAR is based on 5 serial measurements of the rise in salivary cortisol over the first hour from awakening (0, 15, 30, 45 and 60 min). In addition, 3 saliva samples at 12 AM, 6 PM, and 11 PM were collected. Saliva home-sampling was performed using Salivettes tubes (Sarstedt, Neubringen, Germany). Participants were trained in the sampling technique at the day of the PET-scan and were carefully instructed not to eat, drink tea or coffee, smoke, or brush their teeth during the first hour from awakening. The mean time-span from awakening to the first sample was 2.773.1 min. Three participants reported a delay of 10 min, the restr5 min. At home, the study participants stored their samples at maximum 5 1C. Within 3 days the saliva samples were sent by 24-h mail and stored at 80 1C. Cortisol concentrations were determined by the Electrochemiluminescence Immunoassay (ECLIA) method on Modular Analytics E170 equipment (Roche, Mannheim, Germany) in one batch. The test–retest variability ranges between 1.5% and 6.1%. The primary outcome measure, CAR, was computed as area under curve from 0 to 60 min from awakening with respect to increase from the baseline awakening cortisol value (AUCi) (Fekedulegn et al., 2007; Pruessner et al., 2003). In addition, the total daily hormonal output was computed as the area under the curve over the full day profile of 8 measurements spanning from awakening to approximately 11 PM (AUCfull_day). AUCi measures were standardized across a 60 min time-span, and AUCfull_day across a 960 min time-span.

2.2.

Statistics

Multiple linear regression was used to test if CAR predicted VOI SERT binding. All models were adjusted for age since previous studies show age-related decreases of SERT binding (Meyer et al., 2001; Reimold et al., 2008). Further, we tested for main effects of 5-HTTLPR status (S- or LG-allele carrier vs. LALA) and 5-HTTLPR status X CAR interaction effects. Follow-up analyses were performed to test the robustness of identified significant associations by adjusting for additional potentially relevant variables (absolute cortisol levels at awakening, daylight minutes, daylight minutes by 5-HTTLPR status (Kalbitzer et al., 2010), BMI (Erritzoe et al., 2010a), openness (Kalbitzer et al., 2009), neuroticism, and Cohen’s perceived stress score). Contingent on observing significant associations, we tested models in an age-restricted subsample (ageo40 years, N=26), in order to exclude that age effects on SERT-binding and/or CAR might

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have biased the results (irrespective of age being entered as a covariate). We tested if cortisol output over the full day (AUCfull_day) correlated with prefrontal SERT binding. Finally, based on earlier observations, we tested if CAR had significant interactions with gender (Brummett et al., 2008; Wust et al., 2009) or neuroticism score (Frokjaer et al., 2010), in predicting prefrontal SERT binding. The analyses and graphical presentations were performed in Instat 3.0b and R2.9.0 (http://www.r-project.org/). Model assumptions were tested graphically by examination of the distribution of the residuals as well as examination of cumulative residual plots against the predicted values and the continuous predictors in the model. No indications of severe misspecifications were detected. p-Values, as estimated by two-sided tests, parameter estimates with standard errors (SE) and 95% confidence limits (CL) are reported when appropriate. p-Values below 0.05 were considered statistically significant.

2.3.

Voxel-based analysis

To cover potentially relevant brain regions not represented in our a priori defined VOIs and heterogeneity within our VOIs, we also conducted a voxel-based analysis. The analysis was restricted to the younger part of the sample (ageo40 years, N=26) to mitigate difficulties arising from spatial registration of more atrophic brains of the elderly subjects. The analysis was conducted with SPM 8 (Wellcome Department of Imaging Neuroscience, London, UK) and comprised the following steps: (1) T1-weighted images were spatial normalized to the Montreal Neurological Institute (MNI152) T1 template. (2) PET images were re-sliced into MNI space using the estimated warp fields with 2 mm isotropic voxels. (3) PET images were spatially smoothed with a Gaussian kernel with 12 mm fullwidth half-maximum. (4) Only voxels within a brain mask (184.685 voxels) defined by mean SERT BPND40.1 were analyzed. (5) A voxelwise multiple regression was conducted with AUCi and age included in the model as covariates. The primary interest was the positive AUCi t-contrast. Significance threshold was set to po0.05, with Family Wise Error (FWE) correction for multiple comparisons at voxel level. We also constructed effect size maps at po0.001, po0.01 uncorrected for multiple comparisons.

3.

Results

Demographic data, paraclinical data, regional SERT BPND and cortisol measures of the study group are presented in Table 1.

3.1.

Effect of CAR on SERT binding

Table 2 shows the results of the multiple regression models adjusted for age. CAR (AUCi) correlated significantly and positively with prefrontal SERT binding (Figure 1) but not with anterior cingulate. The correlation between AUCi and prefrontal SERT binding remained significant when 5-HTTLPR status was included as a covariate (Table 3). Also, the correlation did not substantially change when controlling for BMI, gender, daylight minutes at scan, the personality score on Openness, Neuroticism, or perceived stress at scan, respectively, (0.025opo0.057). Further, the effect of CAR remained when analyzed in more complex models including multiple potentially relevant covariates (Erritzoe et al., 2010a; Kalbitzer et al., 2010) (Table 3). We emphasize that the causal association between CAR and SERT binding cannot be

Table 1 Demographic and psychometric data, SERT BPND, and cortisol parameters. Parameter

Mean value7SD (ranges in brackets)

Age (years) Sex distribution BMI (kg/m2) Smoking Alcohol (units per week) SERT genotype (S- or LG-carrier vs. LALA) Openness score Neuroticism score SCL_GSI (global severity index score) MDI Cohen’s perceived stress score CAR, AUCi AUCfull_day Mean prefrontal SERT BPND Mean anterior cingulate SERT BPND

35.3720.1 (19.7–81.7) 22% Women (7 of 32) 25.073.2 (17.9–32.9) 12.5% Smokers (4 of 32) 8.977.1 (0–25) 34% LALA (11 of 32) 121716 (82–145) 71717 (33–108) 0.1270.11 (0–0.42) 3.372.4 (0–9) 4.773.6 (0–14) 597230 (446–588) 305272515 (598–11,537) 0.5870.079 (0.44–0.81) 0.6670.094 (0.47–0.85)

SERT genotype: genotype of the 5-HTTLPR (serotonin transporter linked polymorphic region) coded as a binary variable; S or LG-carrier or LA homozygous. BMI: body mass index (weight in kg/(height in m)2). SCL_GSI: symptom check-list, global severity index score. MDI: major depression inventory. CAR: cortisol awakening response. AUCi: area under curve with respect to increase from awakening (0–60 min). AUCfull_day: area under curve estimated from 8 diurnal measures and adjusted to 960 min. BPND: binding potential of specific tracer binding (unitless).

clarified using these data, and the effect estimates in Tables 2 and 3 should therefore be interpreted cautiously. Note, that our models and p-values are equivalent with what would be obtained in a partial correlation analysis, which for model B in Table 3 leads to an estimated partial correlation between AUCi and prefrontal SERT of 0.419 (CL [0.082; 0.670]). Voxel-based analysis revealed one cluster centered in Brodmann area 25, including portions of left ventromedial prefrontal and left subgenual anterior cingulate cortex, where CAR showed a significant and positive correlation with SERT BPND (MNI coordinates: x =6, y =22, z =16; p =0.02; FWE correction at po0.05). As indicated by the effect size maps, voxel-based analysis generally supported a prefrontal, mainly orbitofrontal, and fairly symmetrical pattern of correlation (Figure 2).

3.2.

Supplementary analyses

Partial volume correction was considered appropriate since our sample represented a large age range but we also confirmed in the age-restricted sample (N= 26, ageo40 years) that CAR significantly predicted both non-partialvolume corrected (slope =1.04, 95% CL [0.61;2.02],

SERT and cortisol awakening response

289

0.8

Prefrontal Cortex BPND

0.7

0.6

0.5

0.4 −400

−200

0

200

400

600

AUC increase

Figure 1 Scatter plot of the correlation between the cortisol awakening response (AUC increase) and prefrontal SERT-binding adjusted for age.

Table 2

Effect of the cortisol awakening response (AUCi) on SERT binding at VOI-level in a model adjusting for age.

Region

Effect of AUCi, slope estimates (BPND per 1013 mol cortisol/L min)

Standard error

95% Confidence limits (BPND per 1013 mol cortisol/L min)

p-Value

Prefrontal cortex Anterior cingulate

1.50 1.24

0.61 0.75

[0.25; 2.75] [0.30; 3.77]

0.02 0.11

Effects of AUCi evaluated in multiple linear regression analysis adjusting for age, N=32. In prefrontal cortex a negative age effect only reached a trend-level (p=0.06) and age did not tend to predict partial volume corrected SERT BPND in anterior cingulate (p=0.42).

Table 3 Effect of cortisol awakening response (AUCi) on prefrontal cortex SERT binding when analyzed in alternative models of various complexity. Model (covariates)

Effect of AUCi, slope estimates (BPND per 1013 mol cortisol/L min)

Standard error

95% Confidence limits (BPND per 1013 mol cortisol/L min)

p-Value

A. (AUCi, age) B. (AUCi, age, genotype) C. (AUCi, age, genotype, cortisol at awakening) D. (AUCi, age, genotype, daylight, genotype  daylight, BMI)

1.50 1.60 1.71

0.61 0.65 0.69

[0.25; 2.75] [0.26; 2.93] [0.28; 3.14]

0.02 0.02 0.02

1.33

0.70

[0.17; 2.78]

0.07

AUCi: the cortisol awakening response calculated as area under curve with respect to increase from awakening. Genotype: genotype of the 5-HTTLPR (serotonin transporter linked polymorphic region) coded as a binary variable; S or LG-carrier or LA homozygous. Daylight: daylight minutes at day of PET-scan. BMI: body mass index (weight in kg/(height in m)2).

p=0.038), and partial volume corrected prefrontal BPND (slope =1.65, 95% CL [0.17; 3.28], p=0.048), age corrected. If the correlation between CAR and SERT binding in the projection areas would be due to an effect of the number of serotonergic neurons it would be expected to correlate with midbrain SERT binding (and to appear in a more global pattern). This was not the case (CAR correlation with

midbrain SERT BPND, age corrected analysis: slope =1.60, 95% CL [1.47;4.66], p =0.29). This analysis was based on non partial-volume corrected BPND since tissue classification in gray-, white matter, and cerebrospinal fluid is not appropriate in midbrain. We excluded that CAR correlated with non-displaceable tracer binding. Both the area under the cerebellar time–activity

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Figure 2 Voxel-based analysis of positive correlations between the cortisol awakening response (AUCi) and prefrontal SERT BPND in an analysis correcting for age. The analysis supported a primarily prefrontal and fairly symmetrical phenomenon when inspected at the uncorrected level (puncorrectedo0.001 and o0.01). At the FWE corrected level (pcorrectedo0.05) only one cluster centered in left Brodmann area 25 including portions of left ventromedial prefrontal and left subgenual anterior cingulate cortex reached statistical significance. This cluster size was 35 voxels corresponding to a brain volume of 0.28 mL. The local maximum in this cluster was located at the MNI coordinates (x =6, y =22, z =16). The analysis was restricted to the 26 younger individuals (o40 years). 0.8

Prefrontal Cortex BPND

0.7

0.6

0.5

0.4 s or LG LALA

0.3 −400

−200

0

200

400

600

AUC increase

Figure 3 SERT genotype (5-HTTLPR) did not moderate the association between the cortisol awakening response (AUCi) and prefrontal SERT BPND.

curves normalized to the injected dosage per kg and the reference tissue wash-out (k20 in MRTM2) were not correlated with AUCi (p= 0.46, and p=0.86, respectively, age corrected). As expected, a positive CAR was not detected in all subjects (63%) (Wust et al., 2000b). Findings were unaltered when the 3 individuals who reported 10 min delay in baseline awakening sampling were excluded (p =0.049, N=29). Also, delay between awakening and first sample did not predict AUCi (p=0.50). When excluding the 3 individuals where the time interval between CAR and PET assessments was extreme (90, 142 and 149 day, for the rest the mean distance was 11 79 day) the correlation sustained (p=0.03, age adjusted). The absolute level of cortisol at awakening (baseline level) did not significantly (p= 0.85) predict prefrontal SERT binding. Similarly, the full day profile of absolute cortisol output (AUCfull_day) did not correlate significantly with prefrontal SERT (p= 0.13). We tested whether the initial rise (or decrease) isolated as the slope of the line between baseline and cortisol level

at 30 min, correlated with prefrontal SERT. This was the case and supported that our finding was not primarily driven by late time components of CAR (p=0.01, age adjusted).

3.3. 5-HTTLPR genotype effects on SERT BPND, CAR, and interaction analyses No effects of 5-HTTLPR status were observed on VOI SERT BPND when analyzed as a main effect (0.864p40.38) or in a model adjusting for age (0.714p40.59). CAR tended to be higher in S- or LG-allele carriers (p= 0.07, age corrected) but when tested in an interaction analysis, S- or LG-carriers did not show a stronger coupling between prefrontal SERT binding and CAR (p=0.86) (Figure 3). Since increased cortisol response to psychosocial stressors has been seen in SS homozygotes (Gotlib et al., 2008; Way et al., 2010), we also tested if CAR differed between individuals with two low-expressing copies (S or LG), N=9, as opposed to LA carriers. This was not the case (p=0.54).

SERT and cortisol awakening response Neuroticism, age, or gender did not significantly moderate the effect of AUCi on prefrontal SERT binding as tested in 3 separate interaction analyses (N  AUCi: p=0.85, gender  AUCi: p=0.97, age  AUCi: p=0.83).

4.

Discussion

We found a positive association between prefrontal SERT binding and HPA-axis cortisol response to awakening, which was independent of SERT (5-HTTLPR) genotype, as well as of potential confounders. A voxel-based analysis supported a prefrontal pattern of association and a cluster centered in left Brodmann area 25, including portions of ventromedial prefrontal and subgenual anterior cingulate cortex, showed a particularly strong association between SERT binding and HPA-axis response to awakening. To our knowledge only one human study has directly investigated the relationship between HPA-axis dynamics and cerebral SERT. Reimold et al. (2011), observed a negative correlation between thalamic SERT binding and HPA-axis output in terms of cortisol response in the dexamethasone–corticotropin test in both a group of mixed unipolar depressed and OCD patients and in healthy controls. The same tendency was observed in anterior cingulate and parts of the prefrontal cortex. In contrast to these findings, we observed a positive association between cerebral SERT binding and HPA-axis output in the prefrontal cortex. This discrepancy might be due to differences in the two HPA-axis output measures and their relations to SERT since, in contrast to CAR, dexamethasone-suppression of the HPA-axis represents the inhibitory capacity of the system in a highly stimulated condition. Further, the observations by Reimold et al. are partly based on psychiatric patients transiently taken off medication (5–17 day) which may have influenced the results since, e.g., SSRIs are known to downregulate SERT for several weeks after withdrawal (Meyer, 2008). On a trend level our findings suggest that the S- or LGallele carriers exhibit larger HPA-axis responses to awakening relative to LA/LA. This trend is in agreement with some (Chen et al., 2009), but contrasts other observations (Wust et al., 2009). Whether HPA-axis responses to psychosocial stressors and to awakening share common physiological features or common genetic influence is not clear (Bouma et al., 2009). Yet, some studies support that those two measures of HPA-axis output are correlated (Chen et al., 2009; Gotlib et al., 2008) suggesting that CAR indexes HPAaxis reactivity. Notably, individuals with low-expressing 5-HTTLPR variants show higher cortisol responses when exposed to psychosocial stress (Alexander et al., 2009; Gotlib et al., 2008; Way et al., 2010). We emphasize that no causality can be attributed to the observed association between CAR and SERT binding. However, we speculate that the association may reflect (1) cortisol exposure influence on cortical SERT binding, (2) prefrontal serotonergic influence on HPA-axis activity, and/ or (3) genetic or early brain developmental effects on both prefrontal SERT and HPA-axis activity. Endogenous serotonin does not compete with [11C]DASB at the binding site in a detectable manner as demonstrated in humans (PraschakRieder et al., 2005; Talbot et al., 2005). Therefore, the

291 observed correlation is considered unlikely to relate directly to synaptic levels of serotonin at the time of the PET-scan. Glucocorticoid exposure may lead to higher SERT-protein levels as supported by in vitro studies of human cell lines (Glatz et al., 2003) and lead to higher cortical SERT binding as supported by studies in newborn rats (Slotkin et al., 2006). While this may offer some explanation for the association between higher CAR and higher cortical SERT it does not directly shed light on the regional specificity of the association we observed. Alternatively, we speculate that prefrontal serotonergic tonus as reflected in SERT binding (Erritzoe et al., 2010b) might be critical to the inhibitory tonus on the HPA-axis. Indeed prefrontal cortex is involved in inhibitory feedback control on the HPA-axis (Herman et al., 2005; Pruessner et al., 2010). Rat studies support that serotonin levels in the medial prefrontal cortex are critical for cessation of behavioral stress and fear responses (Forster et al., 2006, 2008; Hashimoto et al., 1999). Further, human intervention studies support that serotonergic tonus modulates the HPA-axis activity; Lowering central serotonin by acute tryptophan depletion in healthy volunteers leads to a neuroendocrine stress response, in the absence of perceived stress, possibly through a disinhibitory effect on the axis (Hood et al., 2006). 5-HTTLPR status only modestly influences SERT binding in the adult brain (Willeit and Praschak-Rieder, 2010). That is, low SERT binding per se does not necessarily characterize individuals that are vulnerable to stress (Karg et al., 2011) or explain the observed increase in HPA-responsiveness seen in individuals carrying the low-expressing 5-HTTLPR variants (Alexander et al., 2009; Chen et al., 2009; Gotlib et al., 2008; Mueller et al., 2010; Way and Taylor, 2010). Indeed, even though low expressing 5-HTTLPR variants seemed to be related to high CAR in our sample, as may be expected, high CAR was not coupled to low SERT binding. Rather, our observations are consistent with a neurodevelopmental explanation to how the lowexpressing 5-HTTLPR variants mediate increased HPA-axis reactivity or sensitivity to psychosocial stress (Sibille and Lewis, 2006), e.g. by early life effects on brain maturation (Ansorge et al., 2004). Yet, such a neurodevelopmental explanation does not exclude that 5-HTTLPR might influence the flexibility of SERT adaptation to robust environmental challenges in adult life. That is, the homeostatic capacity of the system might depend on sufficient down-regulation of prefrontal SERT in the context of risk factors or challenges influencing HPA-axis activity. Future intervention studies are needed to elucidate whether such compensatory adaptations may be impaired in individuals carrying low-expressing 5-HTTLPR variants. Interestingly, an increased HPA-axis output in terms of CAR (Mannie et al., 2007; Vreeburg et al., 2010), and reduced inhibitory control on the HPA-axis (Modell et al., 1998) appears to be a trait marker for mood disorders as demonstrated in healthy individuals at familial risk. Likewise, elevated CAR is a risk factor for developing major depression (Adam et al., 2010) and is observed both in recovered and in currently depressed patients (Bhagwagar et al., 2003, 2005; Vreeburg et al., 2009). Also, high cortisol excretion is a marker of high risk for depression relapse (Ising et al., 2007; Zobel et al., 2001), and the prophylactic efficacy of SSRIs in depression is related to normalized HPAaxis reactivity (Schule, 2007). Therefore, the association between prefrontal SERT availability and HPA-axis activity

292 may be critical for both risk of developing depression and likelihood of remaining healthy after remission. Notably, we observed a particularly strong association between CAR and SERT-binding in the subgenual anterior cingulate cortex, a brain region where structural and functional deficits has consistently been linked to mood disorders (Hamani et al., 2011). Also, deep brain stimulation targeting this exact region can improve symptoms in 60% of patients with treatment resistant depression 1 year from surgery (Lozano et al., 2008). Remarkably, in rodent models of deep brain stimulation, the antidepressant effect appears to be dependent on integrity of the serotonergic system (Hamani et al., 2010). We suggest that deep brain stimulation targeting subgenual anterior cingulate cortex may in part act by dampening dysfunctions in HPAaxis responsiveness. In a previous study, we observed that mentally healthy individuals at high familial risk for mood disorders have lower SERT binding in dorsolateral prefrontal cortex as compared to individuals at low risk (Frokjaer et al., 2009). In light of the present observation of a coupling between low prefrontal SERT binding and low CAR this raises the question whether low prefrontal SERT is a relevant compensatory mechanism supporting stress tolerance and mental health despite an individual’s familial risk. In line with this, some studies suggest that high SERT-binding is linked to depression and severity of depressive symptoms (Cannon et al., 2007; Meyer et al., 2004). Future longitudinal studies must elucidate if downregulation of prefrontal SERT might represent such a compensatory phenomenon affecting HPAaxis (re) activity. The present findings must be interpreted in the light of the following methodological limitations. First, the sample size available in this study was limited. Therefore, evaluations of gender and genotype effects on SERT binding and on the correlation between frontal SERT binding and CAR are somewhat underpowered. Accordingly, we may well have missed a smaller effect of genotype or gender on SERT binding seen by some. Also, studies in larger samples are needed to conclusively evaluate if 5-HTTLPR genotype grouping would modify the observed correlation between frontal SERT binding and CAR. Second, in some cases the time interval between PET-scan and CAR measurement was more than a month. This may have tended to blur the correlation between the state-dependent components of the correlation between prefrontal SERT binding and CAR. However, when leaving out those observations were the interval was extreme (90, 142, and 149 day) the correlation remained significant (p=0.03). In conclusion we find that in mentally healthy adults, prefrontal SERT binding is positively associated with cortisol responses to awakening. This coupling was not dependent on 5-HTTLPR status. These findings point toward a capacity for prefrontal serotonergic neurotransmission to balance HPA-axis activity in mentally healthy individuals.

Role of the funding source Funding for this study was provided by The John and Birthe Meyer Foundation that donated the Cyclotron and PET scanner. Further funding was provided by Dagmar Marshalls Foundation, Novo Nordisk Foundation, Danish Medical Research Council, The Health Science Faculty, University of Copenhagen, The 1991 Pharmacy

V.G. Frokjaer et al. Foundation, The Lundbeck Foundation, the EU Sixth framework programme (EC-FP6-project DiMI (LSHB-CT-2005-512146)), Sawmill owner Jeppe Juhl and Wife Ovita Juhls Foundation, ‘‘Laegernes forsikringsforening af 1891’’, ‘‘Fonden af 1870’’, and Emmy Kramps Legat. The funding sources had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

Contributors Author Vibe Gedsoe Frokjaer designed the study, wrote the protocol, managed the literature search, and performed image- and statistical-analyses, and wrote the first draft of the manuscript. David Erritzoe managed the PET-experiments. Klaus K¨ ahler Holst supervised the statistical analyses and graphical representations. Peter Steen Jensen managed the digital data handling and computing of cortisol outcome parameters. Peter Mondrup Rasmussen performed the voxelbased analyses. Patrick MacDonald Fisher participated in critical revisions of analyses and interpretations. William Baare and Kathrine Skak Madsen managed the MR-acquisitions and MRI processing. Jacob Madsen managed the radioligand production. Claus Svarer set up the image processing and quantification, Gitte Moos Knudsen supervised the whole project. All authors contributed to and have approved the final manuscript.

Conflict of interest The authors declare that, except for income received from their primary employers, no financial support or compensation has been received from any individual or corporate entity over the past three years for research or professional service and there are no personal financial holdings that could be perceived as constituting a potential conflict of interest.

Acknowledgments We are grateful to the participants. We wish to thank Anita Dole, Dorthe Givard, Lisbeth Andreasen, and the staff at the PET centre, Rigshospitalet for their superb technical assistance. The John and Birthe Meyer Foundation is thanked for the donation of the Cyclotron and PET scanner.

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