Testosterone and reward prediction-errors in healthy men and men ...

SCHRES-06469; No of Pages 12 Schizophrenia Research xxx (2015) xxx–xxx

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Testosterone and reward prediction-errors in healthy men and men with schizophrenia R.W. Morris a,b,c,1, T.D. Purves-Tyson a,b,d, C. Shannon Weickert a,b,c, D. Rothmond a,b,c, R. Lenroot a,b,c, T.W. Weickert a,b,c,⁎ a

Neuroscience Research Australia, Barker St, Randwick, New South Wales 2031, Australia Schizophrenia Research Institute, Liverpool St, Darlinghurst, New South Wales 2010, Australia School of Psychiatry, University of New South Wales, Hospital Rd, New South Wales 2031, Australia d School of Medical Sciences, University of New South Wales, New South Wales 2031, Australia b c

a r t i c l e

i n f o

Article history: Received 4 January 2015 Received in revised form 9 June 2015 Accepted 28 June 2015 Available online xxxx Keywords: Sex hormones Reward Ventral striatum Associative learning Tyrosine hydroxylase Midbrain Androgen receptor Postmortem Testosterone Schizophrenia

a b s t r a c t Sex hormones impact reward processing, which is dysfunctional in schizophrenia; however, the degree to which testosterone levels relate to reward-related brain activity in healthy men and the extent to which this relationship may be altered in men with schizophrenia has not been determined. We used functional magnetic resonance imaging (fMRI) to measure neural responses in the striatum during reward prediction-errors and hormone assays to measure testosterone and prolactin in serum. To determine if testosterone can have a direct effect on dopamine neurons, we also localized and measured androgen receptors in human midbrain with immunohistochemistry and quantitative PCR. We found correlations between testosterone and predictionerror related activity in the ventral striatum of healthy men, but not in men with schizophrenia, such that testosterone increased the size of positive and negative prediction-error related activity in a valence-specific manner. We also identified midbrain dopamine neurons that were androgen receptor immunoreactive, and found that androgen receptor (AR) mRNA was positively correlated with tyrosine hydroxylase (TH) mRNA in human male substantia nigra. The results suggest that sex steroid receptors can potentially influence midbrain dopamine biosynthesis, and higher levels of serum testosterone are linked to better discrimination of motivationallyrelevant signals in the ventral striatum, putatively by modulation of the dopamine biosynthesis pathway via AR ligand binding. However, the normal relationship between serum testosterone and ventral striatum activity during reward learning appears to be disrupted in schizophrenia. Crown Copyright © 2015 Published by Elsevier B.V. All rights reserved.

1. Introduction Testosterone impacts male motivation, competitive drive and social behavior (Spear, 2000; Archer, 2006; Coates and Herbert, 2008; Richards et al., 2009; Morris et al., 2010). In schizophrenia, testosterone levels inversely correlate with negative symptoms, such as lack of motivation, flat affect and social withdrawal (Akhondzadeh et al., 2006; Ko et al., 2007). The negative symptoms of schizophrenia relate to abnormal activity in a fronto-striatal circuit during reward processing (Juckel et al., 2006; Schlagenhauf et al., 2008; Morris et al., 2012, 2014). However, the relationship between testosterone and neural activity during reward processing in schizophrenia is unknown. Valence-specific changes in midbrain dopamine neuron firing are known to underpin successful reward learning from a neurobiological

⁎ Corresponding author at: Neuroscience Research Australia, Barker Street, Randwick, New South Wales 2031, Australia. E-mail address: [email protected] (T.W. Weickert). 1 Present address: School of Psychology, University of New South Wales, New South Wales 2052, Australia.

perspective (Schultz, 2013). Midbrain dopamine neurons projecting to the nucleus accumbens code for reward prediction-errors by increasing firing after unexpected rewards (URs) and by decreasing firing after unexpected reward omissions (UOs) (Schultz et al., 1997). Consistent with this, blood oxygenation level dependent (BOLD) responses in the healthy human ventral striatum reliably show increased activity to URs and decreased activity to UOs (D'Ardenne et al., 2008; Morris et al., 2012). This, and other evidence (Pessiglione et al., 2006; Knutson and Gibbs, 2007) suggests BOLD-related prediction-error signals in the ventral striatum reflect regionally-specific and temporallyspecific input from midbrain dopamine neurons during rewardprocessing. Testosterone may modulate the mesolimbic dopamine activity via direct action on midbrain dopamine neurons. Testosterone can bind to androgen receptors (AR) and after conversion by aromatase to estradiol, to estrogen receptors (ERα and ERβ). In rodents, most studies demonstrate that dopamine neurons express AR and both estrogen receptors (Shughrue et al., 1997; Perez et al., 2003; de Souza Silva et al., 2009; Purves-Tyson et al., 2012). Evidence from conditioned place preference studies in rodents (de Beun et al., 1992; Alexander et al., 1994) and

http://dx.doi.org/10.1016/j.schres.2015.06.030 0920-9964/Crown Copyright © 2015 Published by Elsevier B.V. All rights reserved.

Please cite this article as: Morris, R.W., et al., Testosterone and reward prediction-errors in healthy men and men with schizophrenia, Schizophr. Res. (2015), http://dx.doi.org/10.1016/j.schres.2015.06.030

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R.W. Morris et al. / Schizophrenia Research xxx (2015) xxx–xxx

anabolic steroid addiction in human males (Kanayama et al., 2009, 2010) suggest testosterone can act as a positive reinforcer. Furthermore, oral administration of testosterone to women increases BOLD activation in the ventral striatum during reward anticipation and has the greatest effect in women with low intrinsic appetitive motivation (Hermans et al., 2010). Thus, the effects of administering testosterone on the mesolimbic path are most marked in people with low motivation; however, the relationship between endogenous testosterone and ventral striatum BOLD activity in men, with or without low motivation, is unknown. Our aim was to determine the extent to which endogenous circulating testosterone levels were associated with ventral striatal BOLD activity during prediction error among healthy men and men with schizophrenia, a group typically associated with anhedonia and motivation deficits. We also identified a potential mechanism by which testosterone could be working by determining whether human midbrain dopamine neurons have the capacity to directly respond to circulating testosterone via sex steroid receptors. Our hypotheses were: 1) that testosterone will be positively related to ventral striatal BOLD activity in healthy men during reward prediction-errors; 2) human nigral dopamine neurones will express AR and the level of expression will be related to dopamine synthesis capacity (as measured by tyrosine hydroxylase mRNA); and 3) low levels of testosterone will co-occur with abnormal BOLD ventral striatal activity in men with schizophrenia during reward prediction-errors, and low BOLD activity will be related to negative symptom severity. 2. Materials and methods

using the Positive and Negative Syndrome Scale (PANSS) (Kay et al., 1987). All procedures involving humans or human tissue were approved by the University of New South Wales Human Research Ethics Committees (HREC 07121, 09187, 07261 and 12435) and the South Eastern Sydney and Illawarra Area Health Service HREC (07259, 09187), and written informed consent was obtained from all fMRI participants. See Table 1 for details on the fMRI participant demographics. Six of the men with schizophrenia and eight of the healthy men were included in a previous report on the neural substrate of prediction-errors (Morris et al., 2012). Thus, in the present study, an additional seven healthy men and 12 men with schizophrenia were assessed. 2.2. Hormone samples Fasting intravenous blood samples were collected between 9:45 am and 11 am. Total testosterone was assayed using a solid-phase, competitive chemiluminescent immunometric assay (Siemens Healthcare Diagnostics Products Ltd, UK) and we also assayed prolactin since levels of prolactin can indicate dopamine antagonism by antipsychotic drugs. All hormonal assays were performed by the Prince of Wales Hospital South Eastern Area Laboratory Services. For testosterone, reference ranges were set at 7.2 to 25 nmol/L, sensitivity of assay was 0.7 nmol/ L, and the interassay coefficient of variation (CV) was ≈10.8%. For prolactin, reference ranges were set at 0 to 372 ml U/L, sensitivity of assay was 11.0 ml U/L, the intra-assay CV was 6 and the interassay CV was ≈6.8%. 2.3. fMRI methods

2.1. Participants Fifteen healthy men and 21 chronically ill men with schizophrenia or schizoaffective disorder were recruited for this study. Three patients were excluded for excessive head motion (N2 mm), inadequate task performance (non-responding) or structural abnormalities leaving 18 men with schizophrenia, all of whom were receiving second generation antipsychotic medication. All participants were native English speakers, predominantly right-handed (as determined by the Edinburgh Handedness Inventory) and had no history of head injuries with loss of consciousness, seizures, central nervous system infection, uncontrolled diabetes or hypertension or alcohol or drug abuse in the last five years. Trained clinicians administered the Structured Clinical Interview for the Diagnostic and Statistical Manual IV (SCID) (First et al., 2007), and obtained premorbid and current IQ estimates using the Wechsler Test of Adult Reading (WTAR) and a short 4 subtest form of the Wechsler Adult Intelligence Scale-third edition (WAIS-III), respectively (Wechsler, 1997, 2001). Symptom severity ratings were obtained

2.3.1. fMRI acquisition Imaging was acquired on a Phillips Achieva 3T scanner with an 8 channel bird cage head coil at Neuroscience Research Australia. We acquired 968 whole-brain T2* weighted echoplanar images (EPI) with a gradient echo sequence. The slice thickness was 3 mm with a 1 mm gap, consisting of 31 axial slices in ascending order, with a repetition time (TR): 2000 ms, echo time (TE): 30 ms, flip angle: 90°, matrix: 112 × 112, and field of view (FOV): 240 mm. A T1-weighted high resolution anatomical scan was obtained for each participant for registration and screening. The T1-weighted anatomical scan had a TR: 5.4 ms, TE: 2.4 ms, FOV: 256 mm, matrix: 256 × 256, sagittal plane, slice thickness of 1 mm with no gap, and 180 slices. 2.3.2. Reward prediction task A series of cue-outcome trials were presented in which participants were instructed to predict a reward (an image of nine $50 dollar Australian notes) that was contingent upon presentation of one of

Table 1 Mean (SD) clinical, demographic and hormone results.

Age Years of education WAIS-III IQ score WTAR score Handedness score Testosterone (nmol/L) Prolactin (mlU/L)

SZ (n = 18)a

HM (n = 15)

t (df = 31)

p

33.83 (8.92) 13.83 (2.73) 98.28 (12.75) 107.56 (10.67) 91.89 (14.13) 15.93 (5.49) 222.17 (194.23)

31.29 (8.34) 15.00 (1.92) 108.86 (13.61) 109.64 (7.47) 96.58 (9.15) 17.24 (5.19) 160.86 (134.73)

0.50 1.36 2.26 0.62 1.01 0.68 1.01

0.62 0.19 0.03 0.54 0.32 0.50 0.32

PANSS score (General) (Negative) (Positive)

32.72 (10.13) 16.17 (3.90) 15.06 (7.46)

SZ: men with schizophrenia; HM: healthy males; WAIS-III: Weschler Adult Intelligence Scale, 3rd Edition; WTAR: Weschler Test of Adult Reading; PANSS: Positive and Negative Syndrome Scale for Schizophrenia. a Antipsychotics (no. of patients): olanzapine: 6, clozapine: 3, risperidone: 2, amisulpride: 1, quetiapine: 1, clozapine & aripiprazole: 1, quetiapine & ziprasidone: 1, quetiapine & zuclopenthixol: 1, risperidone & amisulpride: 1, and risperidone & quetiapine: 1.



four distinct playing-cards (the regularly winning “trump” card, see the trial design in Fig. 1). 2.3.3. Pre-scan training To establish expectations between the trump card and the money stimulus each participant was initially trained before entering the scanner until a criterion of six consecutive correct responses occurred. Trials were presented in a pseudo-randomized order to ensure each card was presented at least once in every block of four trials. The trump card was presented on half the trials and during training every trial containing the trump card resulted in reward (i.e., no catch-trials in training). 2.3.4. Scanning task To ensure prediction-errors occurred, catch-trials were introduced during the scan and the 120 trials included 12 trials in which money followed non-trump card hands (unexpected reward: UR) and 12 trials in which no money followed a trump card hand (unexpected omissions of reward: UO). Thus, during the scan the trump card was rewarded 80% of the time. The identity of the trump card was counterbalanced between the card displaying diamonds, squares, circles and triangles across participants. One catch-trial occurred in every block (4-trials) except for the first three blocks in the session which were consistent with training to ensure learned expectations from training were initially

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reinforced in the scanner. Since the outcome was predetermined and pre-training ensured all participants learned the contingency between the trump card and reward, there was little variation in responses among participants. The interval between trials was jittered and ranged between 4051 to 6040 ms during which a cross hair was presented. Participants were required to predict the outcome before presentation of the reward stimulus (money). Any prediction incongruent with the outcome was included as an unexpected event: thus, the obtained number of trials in each condition differed slightly for each participant. Failure to respond resulted in a missed response recorded and these trials were ignored in the analysis. Failure to respond to more than 25% of trials resulted in exclusion as a non-responder (1 patient was excluded for non-responding, as mentioned previously). The duration of the task was 32 min. Stimulus presentation and timing of all stimuli and response events were achieved using Presentation software (Neurobehavioral Systems, CA) on a Dell computer running Microsoft Windows XP (SP3, 2002). Stimulus presentation was automatically synchronized with the onset of each EPI acquisition to ensure accuracy of event timing. Visual stimuli were presented using an MRI-compatible mirrored screen placed at the rear of the MRI scanner and viewed with a system of mirrors arranged in the participants' direct line of sight while lying in the scanner. Response times and accuracy were measured using a Lumina MRI-compatible

Fig. 1. Trial design. The order of events in each trial-type of the reward prediction task. Four different card stimuli were used (diamonds, squares, triangles and circles) in which one card was trump (diamonds in this example). Money (reward stimulus) was either presented or omitted. Predictions were either correct or incorrect.


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two-button response pad (Cedrus Corporation, CA). Vision correction was provided as needed with a set of MRI-compatible glasses.

2.4. Immunohistochemistry Fresh frozen post-mortem tissue provided by the New South Wales Tissue Resource Centre (NSW TRC) from five normal adult humans (mean age = 52.8 years, mean pH = 6.47, mean postmortem interval = 17.2 h, 3 males, 2 females) was blocked perpendicular to the long axis of the CNS to include the quadrageminal plate dorsally and the entire midbrain tegmentum ventrally. Brain slices were cut on a cryostat (14 μm) at the level of the exit of the oculomotor nerve, collected onto gelatin-subbed slides, and stored at −80 °C. On the day of immunolabeling, tissue was thawed, then fixed (10 min) in 4% paraformaldehyde at 4 °C. For the immunofluorescent study, a donkey serum (10% in PBS) block was applied prior to simultaneous incubation with tyrosine hydroxylase (TH) mouse monoclonal antibody (MAB318, Chemicon at 1:250) and androgen receptor (AR) rabbit polyclonal (PA1-110 Affinity BioReagents 1:100) antibody. Secondary antibodies were added together at a 1:1000 dilution (A21202 Donkey antimouse 488, A21207 Donkey anti-rabbit 594, Invitrogen). DAPI nuclear stain was performed in a 1:1000 dilution followed by a copper solution (5 mM CuSO4 in 50 mM AmmAc pH 5.0) to reduce autofluorescence prior to coverslipping.

2.5. Quantitative real-time PCR (qPCR) analysis For mRNA quantification, frozen midbrain tissue at the level of the oculomotor nerve from 19 male schizophrenia cases and 20 matched control males were provided by the NSW TRC (post mortem midbrain cohort; Table 2). Total RNA was extracted with Trizol (Life Technologies, Scoresby, Australia) from midbrain tissue cryostat generated slices (6 × 60 μm thick) where the substantia nigra was excised based on TH immunolabeling of adjacent slide-mounted sections. RNA quality was determined using the Agilent Bioanalyzer 2100 (Agilent Technologies, Palo Alto, CA) and cases with RIN below 5.0 were removed from analysis. cDNA was synthesized using the Superscript III First strand synthesis kit (Life Technologies). qPCR analysis was conducted as reported previously (Weickert et al., 2010). Taqman gene expression assays (Applied Biosystems, Forster, CA) were used for three house keeper control mRNAs (β-actin, Tata-binding protein, ubiquitin C), tyrosine hydroxylase and androgen receptor mRNAs. None of the house keeping genes, nor the geometric mean of the three, varied between schizophrenia patients and controls (for geometric mean, t = 0.745; df = 55; p = 0.46). Serial dilutions of pooled cDNA from all samples were included on every qPCR plate for quantification of sample expression by the relative standard curve method. All qPCR reactions were performed in triplicate. Expression levels of the triplicate mean were then normalized to the geometric mean of the three housekeepers.

Table 2 Demographic detail for the midbrain postmortem cohort.

N (male only) Age at death PH Postmortem interval (PMI) RNA integrity (RIN) Duration of illness (yr) (range) Daily chlorpromazine (CPZ) mean (mg) Last recorded dose CPZ dose (mg) Lifetime CPZ dose (g)

Control

Schizophrenia

20 49.35 ± 12.19 6.63 ± 0.26 30.63 ± 9.29 5.55 ± 1.14

19 49.0 ± 11.93 6.51 ± 0.19 35.76 ± 18.25 5.61 ± 1.48 23.75 (3.5–43) 669.32 ± 273.12 659.26 ± 550.57 6488.99 ± 4581.86

Data are mean ± SD. Values in brackets indicate age range. There were no significant differences in demographic detail between control and schizophrenia groups.

2.6. Data analysis 2.6.1. Task performance The number of errors in the first three blocks of the card game during the scan (prior to the introduction of catch-trials) was used to calculate the percentage of correct trials and confirm the success of training. The remaining trials (including catch-trials) were also used to calculate the percentage of correct trials to ensure group performance was maintained across the scan. Differences in the percentage of correct trials between groups were tested with independent two-tailed t-tests. 2.6.2. Hormones Mean levels of serum testosterone and prolactin were calculated for each group. Differences between each group were tested with independent two-tailed t-tests. To determine relationships between hormones and symptoms, correlations between serum hormone levels and PANSS positive and negative subscale scores were calculated using Pearson r. 2.6.3. Imaging analyses 2.6.3.1. Preprocessing, first-level and second-level analysis. Image processing and analysis was performed using SPM5. Images for each participant were registered to the first image in the sequence using a rigid-body spatial transformation (6 parameters) and resliced using a 4th-degree b-spline interpolation to correct for head motion. Slice time correction to the first slice was performed using SPM5's Fourier phase shift interpolation to adjust for differences in the time of slice acquisition. Images were spatially normalized to the Montreal Neurological Institute (ICBM452) template using a non-linear 12 parameter affine transformation. All images were smoothed to minimize noise and residual differences in gyral anatomy with a Gaussian kernel set at 8 mm full-width at half-maximum (FWHM). Data sets were also manually screened for scan stability (b2 mm head movement) and successful normalization. The default high-pass filter (128 s) was used to remove slow signal drift and serial correlations were accounted for using the default autoregressive (AR1) autocorrelation model in SPM5. The results from each individual subject were analyzed using a fixed effects model. Eight regressors were modeled in an event-related design defined by the stimulus onset times and the participant's responses. Four regressors of interest representing reward outcomes correctly and incorrectly predicted, expected reward (ER) and unexpected reward (UR), and non-reward outcomes correctly and incorrectly predicted, expected omission of reward (EO) and unexpected omission of reward (UO), and four regressors of no interest including two cue regressors (reward cards and non-reward cards) and two response regressors (predicting reward and predicting non-reward). Six regressors from the motion correction procedure were also included in the firstlevel analysis to account for slight variation due to head movement. Events of interest were modeled as stick functions to represent the transient neural response to errors and each of the eight task regressors were convolved with the canonical hemodynamic response function. Individual beta-images for the four outcome regressors (UR, ER, UO and EO) were taken to a second-level random-effects model to test for group effects in separate ANOVAs. We aimed to identify BOLD responses with a phasic increase to UR (positive prediction-error), a phasic decrease after UO (a negative prediction-error), and no change to predicted rewards in healthy men (Niv and Schoenbaum, 2008; Morris et al., 2012). To achieve this, we tested for regions with positive responses (activation) to UR over the ER baseline (UR N ER), as well as negative responses (deactivation) to UO relative to the EO baseline (UO b EO). This was performed in SPM by testing the conjunction between UR N ER and UO b EO contrasts. To ensure the BOLD response to predicted rewards did not change, we excluded regions according to an F-test of ER and EO (mask p b .05, uncorrected). Using a liberal threshold for the exclusion mask meant that only



voxels without significant activation or deactivation during either the ER or the EO baseline conditions were included in the analysis. After exclusion, the results were threshold at a whole-brain voxel-level FDR = .05 to correct for multiple comparisons and used to create a functional ROI (fROI) for the covariate analyses described below. We also confirmed our earlier results based on subsample of this cohort (Morris et al., 2012) in the present analysis.

2.6.3.2. Confirmation analysis of diagnostic group differences in BOLD. We tested for group differences in prediction-error signals within the mesoaccumbens region (midbrain, caudate and putamen included in a single ROI). As described in our previous report (Morris et al., 2012), the three-way interaction: group × reward × surprise was tested to reveal the location of aberrant prediction-error signals.

2.6.3.3. fROI covariate analysis. Since we were specifically interested in the relationship between hormones and prediction-error signals, the SPM of the conjunction analysis described above was saved as a binary mask that was then used as an fROI in each covariate analysis (see Fig. 2). In this way, we restricted any obtained effect of testosterone to prediction-error signals, rather than non-specific effects of reward or task stimuli. We performed a linear regression of testosterone and prediction-error BOLD responses in each group (df = 13 and 16 for healthy men and men with schizophrenia, respectively). To determine testosterone's association with positive and negative prediction-error signals, the differential BOLD responses (i.e., UR N ER and UO b EO) were analyzed in separate regression models with testosterone as a covariate in each model (similarly, we tested symptom levels, prolactin levels and drug dose as covariates as described below). After each test, we confirmed significant effects of testosterone were not due to changes in the ER or EO baseline by directly testing testosterone's association with each ER and EO baseline. Significant effects with the ER and EO baseline in the opposite direction to the prediction-error effect would suggest some (or all) of the effect of hormone with prediction-errors may be due to a change in ER or EO baseline. We also directly compared the correlation between groups by performing a covariate analysis including both groups with testosterone levels as separate covariates. The group by testosterone interaction directly compared the linear effect of testosterone between groups to identify any potential regions of aberrant response in men with schizophrenia relative to healthy men.

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2.6.3.4. Covariate analysis with prolactin and CPZ. Since reward prediction-error BOLD signals are thought to reflect phasic dopamine signaling (Pessiglione et al., 2006; Knutson and Gibbs, 2007), dopamine antagonism by antipsychotic drugs may attenuate the prediction-error BOLD signal in patients with schizophrenia. To assess whether dopamine antagonism may be responsible for aberrant prediction-error signals in men with schizophrenia, we tested whether prolactin levels (which can be raised with increased dopamine antagonism) or the antipsychotic drug dose was inversely related to prediction-error BOLD signal in schizophrenia. The antipsychotic drug dose was first converted to a daily chlorpromazine equivalent dose (CPZ) to compare across different antipsychotics (Bollini et al., 2008). We included prolactin levels and CPZ dose as covariates in separate linear-regression analyses of the differential BOLD responses (i.e., UR N ER and UO b EO) in the fROI among men with schizophrenia to determine whether any relationship existed between prediction-error signals and these indirect measures of dopamine antagonism. 2.6.3.5. Covariate analysis with PANSS positive and negative symptoms. To determine whether aberrant ventral striatal activity was associated with symptoms in schizophrenia, we tested whether positive or negative symptom scores were related to BOLD responses in the fROI among men with schizophrenia. We included positive and negative symptom scores in separate linear-regression analyses of the ER and EO baseline. For each covariate analysis above, we report significant clusters larger than 25 contiguous voxels at p b .005 within the fROI, resulting in a small volume corrected p = .05. The location of significant peak voxels was identified using the Anatomical Automatically Labeling (AAL) toolbox (Tzourio-Mazoyer et al., 2002) and was confirmed by reference to the Atlas of the Human Brain (Mai et al., 2008). 2.6.4. Analysis of AR and TH gene expression in postmortem substantia nigra Independent sample 2-tailed t-tests were used to test for differences in cohort demographics and housekeeper genes and/or geomean of the housekeepers between control and schizophrenia individuals. For all mRNA measures, Pearson's correlations were performed with demographic variables (pH, RIN, PMI, duration of illness) and Spearman's correlations were conducted with antipsychotic drug measurements. PMI and pH correlated with AR and TH gene expression, as such they were used as covariates when analyzing diagnostic differences in mRNA expression using analysis of covariance. Pearson's partial correlations (controlling for pH and PMI) were conducted to determine the relationship between AR gene expression and TH gene expression. Statistical significance was set at p ≤ 0.05. 3. Results 3.1. Hormone levels were similar in healthy men and men with schizophrenia The mean levels of circulating testosterone and prolactin among healthy men and men with schizophrenia were not significantly different (see Table 1). The correlation analyses among hormone levels, PANSS symptoms severity scores and daily antipsychotic dose (CPZ) failed to reveal any strong, significant correlations (see Table 3). 3.2. Healthy men and men with schizophrenia successfully learned to predict the winning outcome in the reward prediction task

Fig. 2. Functional region-of-interest (fROI). The results of the conjunction analysis in healthy men between positive and negative reward prediction-errors.

The mean (SEM) percent accuracy of predictions in the first three blocks before the introduction of catch-trials during the scan were 86.2 (4.7) and 88.1 (4.1) for healthy men and men with schizophrenia, respectively, n.s. Introduction of the catch trials reduced accuracy in


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Table 3 Pearson correlations (and p-values) among hormones, symptom severity and antipsychotic dose.

PANSS positive symptoms score PANSS negative symptoms score Chlorpromazine equivalent dose

Testosterone

Prolactin

−0.06 (.81) −0.27 (.28) −0.32 (.20)

0.05 (.83) −0.11 (.67) 0.18 (.47)

confirming that the positive linear effect of testosterone was not due to potential BOLD changes during expected reward. Thus, the results are consistent with the neural response to unexpected rewards increasing as testosterone levels increased among healthy men. The largest relationship between testosterone and positive prediction-errors occurred in the right ventral striatum of healthy men with significant effects also occurring in the left putamen (see Table 5).

PANSS: Positive and Negative Syndrome Scale.

both groups during the remaining scanning trials to 68.0 (2.6) and 70.0 (3.4) percent, for healthy men and men with schizophrenia respectively, n.s. 3.3. Prediction-error signals occurred in the ventral striatum of healthy men during reward prediction Our conjunction analysis of positive activation to UR and negative activation to UO revealed prediction-error signals throughout the striatal region of healthy men, with the highest significant peak voxel in the left ventral striatum (see Table 4). These SPM results were used to create the functional region-of-interest (fROI) to test the effects of hormones as covariates in each group (Fig. 2). 3.4. Aberrant prediction-error signals in men with schizophrenia were related to negative symptoms We replicated our earlier report of aberrant prediction-error signals in schizophrenia by testing the three-way interaction: reward x surprise x group (Morris et al., 2012). This revealed significant aberrant prediction-error signals occurred in the ventral striatum of men with schizophrenia (Table 4). We also confirmed that aberrant activity in the fROI was associated with symptom severity in schizophrenia: PANSS negative symptom scores were significantly, positively and strongly correlated with right caudate (ventral striatal) activity during the ER baseline (Fig. 3A and B). Examination of the peristimulus time histogram (PSTH) extracted from the peak voxel in the right caudate confirmed the aberrant activity occurred during the ER baseline in men with schizophrenia (Fig. 3C). The increased response to expected rewards, when there should be no change in BOLD signal, obscured positive prediction-error signals in schizophrenia. A scatterplot of the relationship between negative symptoms and aberrant activity in the right caudate confirms the significant correlation was not due to an outlier (Fig. 3D). 3.5. Testosterone levels were related to positive prediction-error signals in healthy men The fROI covariate analysis revealed a significant positive linear relationship between circulating testosterone levels and striatal activity during positive (UR N ER) prediction-errors among healthy men (Fig. 4A). A follow-up analysis testing if testosterone correlated with the baseline BOLD striatal activity during ER was not significant,

Table 4 Prediction-error signals in healthy men and men with schizophrenia. Group

Region label

Cluster size

t

Prediction-errors in healthy men (UR N ER; UO b EO) Left caudate (vStr) 1724 5.44 Right putamen (vStr) 222 3.42

p

MNI (x, y, z)

b0.001 0.001

−10, 8, −4 26, 22, −2

Reward × surprise × group (Aberrant prediction-errors in schizophrenia) Right putamen 178 2.92 0.002 26, 22, −2 Left caudate (vStr) 164 2.65 0.005 −10, 10, −2 UR: Unexpected reward; ER: expected reward; UO: unexpected omission of reward; EO: expected omission of reward; and vStr: ventral striatum.

3.6. Positive prediction-error signals did not vary with testosterone in men with schizophrenia Covariate analysis failed to reveal any significant relationship between circulating testosterone levels and positive prediction-error related activity (UR N ER) in the fROI among men with schizophrenia. We also directly compared the relationship between testosterone and positive prediction-error signals in men with and without schizophrenia by including both groups in the same covariate analysis and testing the group by testosterone interaction. A significant group interaction occurred in the midbrain and the ventral striatum of the fROI (Table 6). Fig. 4B shows regions where the groups were significantly different. Extraction of the parameter estimates at the highest significant peak voxel in the ventral striatum of healthy men confirmed that the correlation with testosterone was strong and positive (Fig. 4C), while a (weaker) inverse relationship existed in men with schizophrenia in the same region (Fig. 4D). This indicates the source of the interaction was due to a weaker or reversed effect in schizophrenia. There were no group differences in the relationship between positive prediction error signals and testosterone in the opposite direction (i.e., schizophrenia N healthy controls). 3.7. Testosterone was inversely related to negative prediction-error signals in healthy men The fROI covariate analysis also revealed a significant inverse linear relationship between testosterone and negative prediction-error signals (UO b EO) among healthy men. This indicates larger decreases in BOLD responses (i.e., larger negative prediction-error signals) as testosterone increased, suggesting that testosterone levels not only relate to motivationally-relevant increases in BOLD, but also to motivationallyrelevant decreases in BOLD in the ventral striatum of healthy men. Fig. 5A shows the peak voxel in the left putamen of healthy men and Table 5 lists significant peaks throughout the ventral striatum during negative prediction error. There was no significant positive relationship of testosterone with the BOLD response to baseline EO among healthy men; thus, changes in EO baseline do not explain the relationship of testosterone with negative prediction-error activity. 3.8. Negative prediction error signals were not inversely related to testosterone in schizophrenia Among men with schizophrenia, a significant positive linear relationship between testosterone and negative prediction-error signals was shown in the fROI, specifically in the right putamen (Table 5); and this was not due to changes in the EO baseline with testosterone. The positive relationship indicates that negative prediction-error signals (deactivation) were more attenuated with higher (normal) levels of testosterone in men with schizophrenia. Overall, the pattern of results suggests that among men with schizophrenia with higher normal levels of testosterone, motivationally-relevant neural changes in the ventral striatum (increases and decreases) were blunted. The group by testosterone interaction to directly compare healthy men and men with schizophrenia revealed significant group differences in the linear effect of testosterone on left and right ventral striatal activity (Table 6 and Fig. 5B). Extraction of the parameter estimates confirmed the correlations with testosterone were in opposite directions



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Fig. 3. Aberrant prediction-error signals in men with schizophrenia (SZ) correlate with negative symptom scores (PANSS). (A & B) Peak voxel in the right ventral striatum (8, 16, −6) correlating with negative symptoms among SZ; (C) peristimulus time histogram (PSTH) at peak voxel showing mean aberrant response after ER is above zero. Shaded area represents SEM; and (D) scatterplot of the significant correlation between response at peak voxel during ER and negative symptom score in SZ. Thin red lines bounding the linear effect represent 95% confidence intervals (SPM threshold p b .005).

in each group (Fig. 5C and D). There were no other significant group differences from the interaction analysis. 3.9. No evidence that CPZ levels were related to prediction-error signals in men with schizophrenia The linear regression with CPZ revealed no significant relationship with prediction-error signals in the fROI or whole-brain analysis in schizophrenia. We also failed to find evidence that prolactin was inversely related to prediction-error signals in the fROI. However, prolactin was positively related to positive prediction-error signals (UR N ER) in the ventral striatum (fROI) in men with schizophrenia (see Table 5). 3.10. Human midbrain dopamine neurons have potential to respond directly to testosterone Many dopamine neuron tyrosine hydroxylase-positive (TH +) immunopositive cells were identified in the substantia nigra pars compacta (SNpc) in all five human midbrains. Using double-label immunofluorescence we confirmed that human dopamine neurons (TH + green cell in Fig. 6B) in the substantia nigra were immunopositive for AR (red cell in Fig. 6C). Overlap of subcellular distribution of TH and AR in human subtantia nigra was confirmed in the cytoplasm, areas that are yellow due to overlap between TH (green) and AR (red) in Fig. 6D, whereas only AR immunoreactivity was found in the nucleus, purple at arrowhead due to overlap

between DAPI (blue) and AR (red), and TH immunolabeling was more intense in the dendrite as compared to AR (thus, it appears green, see arrow). 3.11. Positive relationship between AR and TH gene expression in the substantia nigra We identified no significant differences in TH or AR gene expression levels in substantia nigra between control and schizophrenia post mortem male brains (For TH mRNA: F = 0.008; df = 38; p = 0.93, for AR mRNA: F = 0.59, df = 38, p = 0.45). Neither TH mRNA nor AR mRNA levels correlated with any of the three measures of chlorpromazine equivalents. For last dose CPZ vs. AR mRNA: N = 19, r = − 0.27, p = 0.251 and last dose CPZ vs. TH mRNA: N = 19, r = − 0.32, p = 0.19, all comparisons between average daily CPZ or total lifetime CPZ and TH or AR mRNA: r's b 0.2 and p's N 0.05. Pearson's partial correlations (controlling for pH and RIN), including data from both control and schizophrenia brains, revealed a significant positive relationship between AR and TH mRNA levels (r = 0.53, df = 33; p = 0.002) (Fig. 6E). 4. Discussion The present study found testosterone levels were correlated with the normal neural response to prediction-errors in the ventral striatum of healthy men and this relationship was significantly different from the


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Fig. 4. Correlations between testosterone and positive prediction errors (UR N ER). (A) Regions of significant correlations between striatal activity and testosterone in healthy men (HM); (B) regions of a significant two-way interaction between group, i.e., HM N men with schizophrenia (SZ), neural activity and testosterone levels in the striatum; (C) the significant correlation between the BOLD parameter estimates (UR N ER) and testosterone at the peak voxel in HM; and (D) the negative (and non-significant) correlation between BOLD parameter estimates (UR N ER) and testosterone in SZ, confirming the significant two-way interaction in B was due to a weaker or reversed effect among SZ. Thin red lines bounding the linear effect represent 95% confidence intervals (SPM threshold p b .005).

correlation found in men with schizophrenia. The relationship with testosterone in the healthy men is consistent with the sex steroid modulation of reward prediction-error signals in the ventral striatum and may reflect changes in dopamine signaling. We also detected testosterone receptors (AR) in the human midbrain consistent with the direct modulation of dopamine by sex steroids and thus, dopamine neurons could

be mediators of the testosterone-related changes we observed in the ventral striatum BOLD signal which is also supported by findings of testosterone directly regulating dopamine related gene expression in male rodents (Purves-Tyson et al., 2012). In addition, in post mortem human male substantia nigra we showed that tyrosine hydroxylase gene expression, an indicator of dopamine synthesis capacity, was positively

Table 5 Relationship of testosterone and prolactin to prediction-error signals in ventral striatum. Hormone/group

Region label

Testosterone and positive prediction-errors (df = 29) Healthy men Right caudate (vStr) Left putamen Men with schizophrenia n.s. Testosterone and negative prediction-errors (df = 29) Healthy Men Left putamen (vStr) Left putamen (vStr) Right putamen (vStr) Left putamen Men with schizophrenia Right putamen Prolactin and positive prediction-errors (df = 16) Men with schizophrenia Left caudate (vStr) Right putamen

Cluster size

t

Pearson

99 107

4.70 4.24

0.76 0.73

b.001 b.001

10, 12, −12 −12, 12, −6

34 37 30 37 93

3.88 3.86 3.83 3.69 3.42

−0.70 −0.69 −0.69 −0.68 0.65

0.001 0.001 0.001 0.001 0.002

−26, 10, −4 −14, 10, −8 34, −18, −4 −34, −16, −6 30, 4, −10

398 279

4.69 4.34

0.76 0.74

b.001 b.001

−2, 10, −8 24, 18, −2

r

MNI (x, y, z) p

vStr: Ventral striatum; and n.s. non-significant.



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Table 6 Group differences in the interaction between testosterone and ventral striatum activity. Group/hormone

t

p

MNI (x, y, z)

Healthy men N schizophrenia: testosterone and positive prediction-errors (df = 29) Left putamen (vStr) 111 Midbrain (SN/VTA) 29 Right caudate (vStr) 54

Region label

Cluster size

3.91 3.59 3.48

b.001 0.001 0.001

−14, 0, 0 10, −12, −4 10, 14, −2

Healthy men b schizophrenia: testosterone and negative prediction-errors (df = 29) Left putamen 119 Right caudate (vStr) 118 Left putamen (vStr) 87

4.19 4.00 3.60

b.001 b.001 0.001

−28, −10, −4 14, 14, −2 −24, 12, −2

vStr: Ventral striatum; and SN/VTA: substantia nigra/ventral tegmental area.

correlated with androgen receptor gene expression, providing evidence in the human male that testosterone signaling and dopamine production may be linked. However, if the BOLD signal was regulated via dopamine activity, the type of modulation by testosterone would not appear to be a simple increase or decrease of dopamine neuronal firing in a unidirectional manner since testosterone was positively correlated with increasing BOLD response to unexpected reward (UR) and negatively correlated to decreasing BOLD signal in response to unexpected omission of reward (UO) in healthy men. Thus, our results suggest that sex steroids influence the control of dopamine neurotransmission in a valence-specific manner to augment or suppress release inversely with reward expectations. This is consistent with evidence that testosterone modulates reward-learning to influence motivated behavior,

rather than acting as a primary reinforcer (de Beun et al., 1992; Alexander et al., 1994). Given testosterone's amplifying effect on anticipatory rewardresponses in women with low intrinsic motivation (Hermans et al., 2010), we also expected to see a beneficial relationship between testosterone and ventral striatal activity in men with schizophrenia who generally have low intrinsic motivation. However, the correlation between testosterone levels and neural responses in the ventral striatum of men with schizophrenia was either nonexistent or in the opposite direction to healthy men (e.g., Fig. 4C versus 4D). Thus, in contrast to our expectations, the present results suggest testosterone was having little effect or even a detrimental effect on ventral striatal reward responses in men with schizophrenia.

Fig. 5. Correlations between testosterone and negative prediction errors (UO b EO). (A) Regions of significant correlations between striatal activity and testosterone in healthy men (HM); (B) regions of a significant two-way interaction between group, i.e., HM b men with schizophrenia (SZ), neural activity and testosterone levels in the striatum; (C) the significant correlation between the BOLD parameter estimates (UO b EO) and testosterone at the peak voxel in HM; and (D) the positive (and non-significant) correlation between BOLD parameter estimates (UO b EO) and testosterone in SZ, confirming the significant two-way interaction in B was due to a weaker or reversed effect among SZ. Thin red lines bounding the linear effect represent 95% confidence intervals (SPM threshold p b .005).


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Fig. 6. Tyrosine hydroxylase (TH) and androgen receptor (AR) immunoreactivity in human midbrain neurons. Immunofluorescence of TH and AR in a human SN neuron; (A) DAPI nuclear stain; (B) TH immunoreactivity; (C) AR immunoreactivity; and (D) localisation of TH in the cytoplasm and AR in the nucleus. The arrowhead indicates the nucleus with AR staining and the arrow indicates an axon with TH staining and no AR. Scale bar = 100 μm. (E) TH mRNA and AR mRNA were positively correlated in the substantia nigra from both control and schizophrenia brains (r = 0.53, df = 33, p = 0.002).

In contrast to our hypothesis, we did not find a strong significant correlation between testosterone levels and negative symptoms in schizophrenia (r = −0.27, n.s.). Some studies with larger sample sizes have found increasing circulating testosterone levels are associated with fewer negative symptoms (Akhondzadeh et al., 2006; Ko et al., 2007); however, other studies of men with schizophrenia receiving maintenance treatment with antipsychotics as opposed to displaying acute psychosis have also failed to show a relationship between negative symptoms and circulating testosterone levels (Taherianfard and Shariaty, 2004; Moore et al., 2013). While differences in sample size makes it difficult to reach firm conclusions, the absence of an association between serum testosterone levels and negative symptoms in our sample is at least consistent with the view that symptoms in schizophrenia do not only vary with levels of circulating sex steroids, but also may be due to sex steroid receptor dysfunction or abnormalities in downstream effectors in the presence of healthy hormone levels. The positive relationship between testosterone levels and mesolimbic function in the healthy males suggests that dopamine neurotransmission is normally sensitive to circulating testosterone. A potential mechanism by which testosterone exerts its effects is indicated by our rodent and primate molecular studies, as well as the AR and TH expression levels reported in the current study. In particular, we have found that circulating testosterone levels in adolescent male rhesus macaques correlate with dopamine synthesis potential, as inferred from increased striatal tyrosine hydroxylase levels — the rate-limiting step in dopamine biosynthesis (Morris et al., 2010). Our studies in rodents also find that testosterone triggers a positive feedback loop to change the level of dopamine receptors and breakdown enzymes and dopamine transporters via AR action (Purves-Tyson et al., 2012, 2014). Our current study of human post mortem substantia nigra in patients and controls also found a positive association between a potential functional expression of AR and tyrosine hydroxylase. These converging lines of evidence add support to the possibility that testosterone can directly modify dopamine neurotransmission in the mesolimbic path (Sinclair et al., 2014). The enhancement of the mesolimbic response with testosterone did not occur in men with schizophrenia, even though they appeared to have healthy levels of testosterone and a separate cohort displayed normal midbrain TH/AR mRNA levels. The prediction-errors we found among men with schizophrenia were obscured by aberrant responses during expected rewards (baseline), which raises the problem of why

the prediction-error response was not enhanced above baseline in the context of normal testosterone levels. Given the enhancement of dopamine transmission by testosterone we have observed elsewhere in humans, macaques and rodents (as described above), we speculate that sex steroid related dysfunction in schizophrenia may be responsible for the attenuated response to motivationally-relevant stimuli. The site of dysfunction is likely downstream from the androgen receptor, given that AR and TH mRNA was positively correlated in post-mortem substantia nigra tissue from people with schizophrenia. Instead, we suggest that dysfunction at other points in the downstream pathway (e.g., DAT or VMAT) could derail normal testosterone modulation of dopamine neurotransmission, even in the presence of normal sex steroid receptor action. Thus, we speculate that the lack of relationship between testosterone and ventral striatal responses in schizophrenia may be due to a failure of testosterone to properly regulate other mRNAs encoding proteins that control dopamine neurotransmission, i.e., release and reuptake. However, whatever the molecular mechanism, since goaldirected behavior is guided by reward prediction (Morris et al., 2014), compromised prediction-error signaling may have far-reaching consequences on daily motivation and successfully achieving goals related to education and employment for people with schizophrenia. There were several limitations of the present study relating to the measurement of testosterone and medication effects. The confirmation we report here included data from 14 participants in the original study of reward prediction-errors (SZ = 6, HA = 8) so it does not represent a pure replication of our earlier study with an independent sample. We did not estimate an a priori sample size for the new analysis and so while the correlations we observed were substantial, the confidence intervals around those estimates have a wide range. Our patient sample was treated with second-generation antipsychotic medications, which all have a common dopamine D2 receptor antagonistic effect (Seeman and Tallerico, 1998, 1999), thus, we cannot entirely rule out the possibility that disruption to striatal function among our patient group was due to dopamine antagonism. However, we found no relationship between CPZ and ventral striatal BOLD responses in schizophrenia or to AR or TH mRNA in the midbrain, which is consistent with our view that our results are due to pathology in the illness rather than a medication effect. Another potential limitation is that we measured circulating serum testosterone levels, which are an indirect measure of the actual levels of testosterone reaching the neurons within the brain. The majority of testosterone (N98%) is strongly bound to sex hormone-binding protein or



weakly bound to albumin, leaving only a small proportion free to diffuse into the tissue and bind with sex steroid receptors. However, free testosterone levels correlate with total testosterone and circulating levels are often used as an index of bioavailable testosterone (Vermeulen et al., 1999). In summary, the present results show for the first time, that testosterone may modulate ventral striatal reward sensitivity in healthy men, putatively by directly acting on midbrain dopamine neurons signaling prediction-errors. However, testosterone was uncoupled or negatively coupled with striatal activity in men with schizophrenia. While the reasons for this are not known, it is possibly due to dysfunctional sex steroid receptor signaling in the midbrain of people with schizophrenia (Weickert et al., 2008). This sex steroid receptor dysfunction may be critical during periods of elevated hormone levels, such as during adolescence, when men in the prodromal phase prior to their first psychotic break are more likely to fail at school, work and social relationships. Further research on the effects of testosterone on the prodromal phase during reward processing would be important to understand the genesis of motivational deficits in schizophrenia and all stages of the illness may benefit from sex hormonal modulation which may improve prediction error signaling and motivation. Contributions RW Morris designed the study in part, organized and analyzed the data, and wrote and edited the manuscript. TD Purves-Tyson managed qPCR, contributed to analysis, and edited the manuscript. CS Weickert designed the study in part, managed the study in part, and edited the manuscript. D Rothmond performed immunohistochemistry, managed RNA extraction and cDNA systhesis, and edited the manuscript. R Lenroot assessed patients and edited the manuscript. TW Weickert designed the study in part, wrote the protocol, assessed patients, managed the study in part, and edited the manuscript. All authors contributed to and have approved the final manuscript. Funding body agreements and policies This work was supported by the University of New South Wales School of Psychiatry, National Health and Medical Research Council (NHMRC) Project Grant #568807, Neuroscience Research Australia, the Schizophrenia Research Institute (utilizing infrastructure funding from the NSW Ministry of Health and the Macquarie Group Foundation), and the Australian Schizophrenia Research Bank (supported by the NHMRC, the Pratt Foundation, Ramsay Health Care, and the Viertel Charitable Foundation). CSW is a recipient of a NHMRC (Australia) Senior Research Fellowship (#1021970). RWM is presently supported by NHMRC Project Grant #1069487, and the Australian Research Council Centre of Excellence in Cognition and its Disorders. Conflict of interest CSW is a recipient of a NHMRC (Australia) Senior Research Fellowship (#1021970). RWM is presently supported by NHMRC Project Grant #1069487, and the Australian Research Council Centre of Excellence in Cognition and its Disorders. All authors declare no relevant financial interests or potential conflicts of interest. Acknowledgments The authors thank Alice Rothwell for skilled laboratory support and editing assistance. Midbrain postmortem tissues were received from the New South Wales Tissue Resource Centre (NSW TRC) at the University of Sydney. NSW TRC is supported by the National Institute on Alcohol Abuse And Alcoholism of the National Institutes of Health under Award Number R28AA012725 and the Schizophrenia Research Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank Toni McCrossin and Julia Stevens at the New South Wales Brain Bank for the provision of midbrain tissue sections and collation of clinical demographics for the post mortem cohort.

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