The Administration of the Opioid Buprenorphine Decreases Motivational Error Signals

The administration of the opioid buprenorphine decreases motivational error signals

Pfabigan, D. M.1,2*, Rütgen, M.2, Kroll, S. L.3, Riečanský, I.2,4, & Lamm, C.2*

1 Department of Behavioural Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Sognsvannsveien 9, 0372 Oslo, Norway 2 Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Liebiggasse 5, 1010 Vienna, Austria 3 Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Psychiatry Building, Entrance 27, Floor 9, 581 85 Linköping, Sweden 4 Department of Behavioural Neuroscience, Institute of Normal and Pathological Physiology, Centre of Experimental Medicine, Slovak Academy of Sciences, Sienkiewiczova 1, Bratislava 81371, Slovakia * Corresponding authors: Daniela M. Pfabigan, PhD Department of Behavioural Medicine Faculty of Medicine, University of Oslo Sognsvannsveien 9 0372 Oslo, Norway [email protected]

Claus Lamm, PhD Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna Liebiggasse 5 1010 Vienna, Austria [email protected]

Markus Rütgen: [email protected] Sara L. Kroll: [email protected] Igor Riečanský: [email protected]

Running head: Buprenorphine and errors Word count: 4620

This is a post-peer-review, pre-copyedit version of an article published in Psychoneuroendocrinology. The final authenticated version will be available online at: https://doi.org/10.1016/j.psyneuen.2021.105199

1 Abstract (262 words)

While opioid addiction has reached pandemic proportions, we still lack a good understanding of how the administration of opioids interacts with cognitive functions. Error processing – the ability to detect erroneous actions and correct one’s behaviour afterwards - is one such cognitive function that might be susceptible to opioidergic influences. Errors are hypothesised to induce aversive negative arousal, while opioids have been suggested to reduce aversive arousal induced by unpleasant and stressful stimuli. Thus, this study investigated whether the acute administration of an opioid would affect error processing.

In a double-blind between-subject study, 42 male volunteers were recruited and received either 0.2 mg buprenorphine (a partial µ-opioid receptor agonist and κ-opioid receptor antagonist) or a placebo pill before they performed a stimulus-response task provoking errors. Electroencephalograms (EEG) were recorded while participants performed the task.

We observed no group differences in terms of reaction times, error rates, and affective state ratings during the task between buprenorphine and control participants. Additional measures of adaptive control, however, showed interfering effects of buprenorphine administration. On the neural level, decreased Pe (Error Positivity) amplitudes were found in buprenorphine compared to control participants following error commission. Further, frontal delta oscillations were decreased in the buprenorphine group after all responses.

Our neural results jointly demonstrate a general reduction in error processing in those participants who received an opioid before task completion, thereby suggesting that opioids might have indeed the potential to dampen motivational error signals. Importantly, the effects of the opioid were evident in more elaborate error processing stages, thereby impacting on processes of conscious error appraisal and evidence accumulation.

Keywords: opioids, buprenorphine, error processing, Pe, delta oscillations, motivation

2 1. Introduction

The misuse of prescription opioids in pain treatment such as oxycodone, fentanyl, and buprenorphine has increased to pandemic proportions over the last years (Rudd et al., 2016). The long-term health burden remains to be determined as our knowledge concerning the impact of opioids on the human mind and body is still limited. Opioids (in particular µ-opioid receptor agonists) are linked to pain reduction and pleasure enhancement in humans and animals (Kringelbach and Berridge, 2009; Leknes and Tracey, 2008), suggesting that the opioid system regulates affective states (Koob and Le Moal, 2001). In addition to pain, opioids are reported to attenuate also other aversive experiences such as breathlessness (Hayen et al., 2017), psychosocial stress and depressive symptoms (Bershad et al., 2015; Peciña et al., 2019). Supporting the notion of this broad activity spectrum, opioid receptors are distributed widely throughout the human brain and co-localized with receptors from other neurotransmitter systems involved in motivation and cognition (Fields and Margolis, 2015). Adding a functional explanation to their widespread distribution, a recent review (van Steenbergen et al., 2019) proposed that opioids might (i) increase subjective value of reward, and (ii) reduce aversive arousal in response to unpleasant stimuli. The current study tested the second part of this prediction in regards to error processing, as errors are important learning signals assumed to be accompanied by negative affective arousal (Inzlicht et al., 2015).

Providing a link between error commission and opioids, animal studies suggest that the endogenous opioid system may play a role during error processing as it is implicated in the formation of so-called (reward) prediction error signals (RPE; Fields and Margolis, 2015). For example, Cole and McNally (2007) demonstrated that µ-opioid receptors contributed to the generation of prediction error signals during fear conditioning in rats. So far, there is preliminary evidence that opioids also play a role for prediction errors in humans. Peciña et al., (2013) observed that the discrepancy between the predicted pain signal and the actually perceived pain stimulus reflected a prediction error signals and larger prediction errors were associated with higher µ-opioid receptor activity. In a behavioural study, van Steenbergen et al. (2017) observed increased post-error slowing after error commission when participants received a non-selective opioid receptor antagonist (naltrexone) compared to a placebo. This suggests that blocking the endogenous opioid system may increase adaptive behavioural control, possibly because it increases the negative arousal response following cognitive conflict and errors.

3 In an electroencephalography study, Pfabigan et al. (2015) investigated volunteers with different polymorphisms of the prodynorphin gene, which codes for the precursor of endogenous dynorphins (moderately selective agonists at the κ-opioid receptor; Chavkin et al., 1982). Participants with high levels of prodynorphine gene expression and thereby increased κ-opioid receptor activation showed enhanced early error processing markers. More indirect evidence comes from studies testing individuals with µ-opioid-mediated addictions that suggest a trend for impaired electrophysiological error processing (Chen et al., 2013).

In this study, we assessed both event-related potentials (ERPs) as well as time-frequency measures to provide a thorough analysis of opioidergic effects on error processing. While ERPs map neural processes phase-locked to correct and erroneous responses in the millisecond time range, time-frequency analysis allows characterizing both phase- and non- phase locked activity (Pfurtscheller and Lopes da Silva, 1999). The Error-Related Negativity component (ERN; Falkenstein et al., 2000; Gehring et al., 1993) is a fronto-central negative deflection within 100 ms after error commission. It is an automatic signal assumed to reflect prediction errors during reinforcement learning (Holroyd and Coles, 2002), or signalling cognitive conflict (Botvinick et al., 2001), motivational error significance (Gehring and Willoughby, 2002), or a threat-monitoring signal (Weinberg et al., 2012). The ERN is followed by the positive-going Error Positivity (Pe) at posterior sites (Falkenstein et al., 2000), with more positive amplitudes about 200–400 ms after errors. Pe amplitude variation is considered reflecting conscious affective processing of errors (Ullsperger et al., 2014). As time-frequency measures, we assessed frontal midline theta (FMΘ; ~4-8 Hz) and delta oscillations (~1-3 Hz) over fronto-central electrodes in the first 100ms after error commission. Recent research suggests that FMΘ might be a key neural mechanism of the medial prefrontal cortex to guide cognitive control processes indicating the requirement of top-down control (Cavanagh and Frank, 2014; Cavanagh et al., 2012). Error-related delta oscillations have been primarily associated with error monitoring (Cohen and Cavanagh, 2011), while a few other studies suggested that they might also reflect more elaborate processing stages, such as affective and motivational task relevance (Bernat et al., 2015; Knyazev, 2007, 2012).

This study investigated whether opioid administration interferes with error processing. This is a crucial assessment since intact error processing -the ability to detect incorrect responses- is a prerequisite for successful behavioural adaptation and learning. Error commission has been proposed to induce an aversive negative arousal response (Inzlicht et 4 al., 2015) as it constitutes a cognitive challenge triggering adaptive control. Indirect evidence for this assumption comes from studies assessing affective responses after errors with psychophysiological methods. Facial electromyography (Dignath et al., 2019; Elkins-Brown et al., 2017; Lindström et al., 2013) and startle responses (Hajcak and Foti, 2008) suggest that error commission is accompanied by an early negative affective response. The current study probes the suggestion of a dampening of negative affective responses under the administration of opioids (van Steenbergen et al., 2019) by investigating behavioural and neural measures of error processing in a double-blind, placebo-controlled study. One group of participants was administered the opioid buprenorphine (a µ-opioid partial agonist and κ- opioid antagonist), while a second group was taking an inert pill before a simple stimulus- response task provoking errors was administered. If opioids dampen the assumed negative affective responses accompanying error commission as suggested by van Steenbergen et al. (2019), errors might partly lose their inherent significance, and thereby they might be perceived as less aversive and threat-signalling. This could be linked to anti-depressant like effects in humans (Bershad et al., 2018; Pizzagalli et al., 2020). Thus, we hypothesized that behavioural and neural measures of error commission are diminished after opioid administration compared to placebo.

2. Material and Methods

2.1 Study design

This between-subject, double-blind, placebo-controlled study consisted of one session in which participants received either 0.2mg buprenorphine (BUP) or a placebo (lactose-starch pill) sublingually.

2.2 Participants

A priori power analysis suggested a sample size of 34 participants to detect medium- sized effects in a mixed ANOVA design (details in Supplementary Materials). Initially, we recruited 42 healthy right-handed male volunteers aged between 20-40 years. Only men were recruited to minimize effects of sex-related pharmacokinetic effects of BUP administration (Moody et al., 2011) –see Supplementary Materials, section 6, for our screening procedure. The final sample consisted of 35 participants of which 16 had received buprenorphine (BUP) and 19 a placebo (see Table 1). Written informed consent was obtained from all participants

5 prior to the experiment. The study was conducted in accordance with the Declaration of Helsinki (7th revision, 2013) and was approved by the Ethics Committee of the Medical University of Vienna (EK-Nr. 661/2011).

2.3 Drug

Participants received either 0.2mg buprenorphine (BUP; Temgesic®, Reckitt Benckise Pharmaceuticals) or an inert placebo pill (with the same physical appearance, no distinct taste) sublingually. BUP is a partial agonist at the µ-opioid receptor, but an antagonist at δ- and κ-opioid receptors. It is prescribed to treat moderate to severe pain and is in use during opioid-replacement therapy (Schmidt-Hansen et al., 2015). The current dose of 0.2mg is considered a rather low dose (Zacny et al., 1997), and was chosen in accordance with previous studies (Bershad et al., 2018; Bershad et al., 2016). BUP peak plasma levels after such a dose and administration route occur 90-360 minutes after administration (Mendelson et al., 1997), which is well within the time window in which the experimental tasks were administered. Please refer to Supplementary Materials, section 2, for a more detailed description of buprenorphine and its pharmacological action.

2.4 Procedure

Upon arrival, participants gave written informed consent, provided a urine sample, and after passing this drug test they performed a pain calibration procedure that is outside the scope of the current report (duration: 15 minutes). Subsequently, a physician (IR) measured blood pressure and then administered the sublingual pill. He provided the information that participants were administered either an approved and effective painkiller or a placebo pill without any effects. Inside the EEG chamber, participants’ heads were positioned in a chin rest. First, participants performed an experimental empathy task for 45 minutes, unrelated to the present research questions. Afterwards, a stimulus-response task was administered to assess error processing, taking place approximately 75-105 minutes after drug/placebo administration.

2.5 Experimental task

A modified version of the Eriksen flanker task (Eriksen and Eriksen, 1974) was used to assess error processing. Participants had to indicate target letters (H, S, K, or C) in the middle of a five-letter string via button press (index and middle fingers of their right hand). The white letter strings were either congruent (HHHHH, SSSSS) or incongruent (HHSHH,

6 SSCSS) and presented centrally on black background. Each trial started with the presentation of a fixation cross for 200ms, followed by the four outer letters for 100ms. Next, the central letter was blended in this array for 35ms. Afterwards, participants had 870ms to give a response. Performance feedback was only presented in case participants responded too slow, demanding faster responses (duration 1000ms), otherwise a fixation cross was blended in for 1000ms, followed by an inter-stimulus interval of 800-1200ms with the fixation cross. After 24 training trials, participants performed 304 trials with 50% congruent and 50% incongruent stimuli. Participants were given breaks every 50 trials, during which they provided affective ratings of how worried and comfortable they felt during the last trials on 9-point Likert scales.

2.6 Questionnaires

Prior to the experiment, participants filled in online questionnaires (BDI-II; Beck et al., 2006; trait version STAI, Laux et al., 1981; and PSWQ; Meyer et al., 1990) to assess anxiety- and depression-related variables, which possibly moderate error-related brain activity (Moran et al., 2015; Moser et al., 2013; Olvet and Hajcak, 2008). At the beginning of the experimental session, participants filled in a mood questionnaire (MDBF; Steyer et al., 1997). Before starting the Flanker task, they filled in the Drug Effects Questionnaire (DEQ-5; Morean et al., 2013). At the end of the experiment, a parallel version of the MDBF was filled in. Afterwards, participants were asked whether they experienced drug side effects.

2.7 Electrophysiological recordings and data analysis

EEG was recorded from 59 equidistant electrodes. Initial signal preprocessing and artefact correction was conducted using EEGLAB (Delorme and Makeig, 2004). Please refer to Supplementary Materials, sections 7 and 8, for further details on data collection and EEG pre-processing.

To assess error-related ERPs and neural oscillations, data were epoched response- locked for correct and erroneous trials. On average, 221 correct trials and 18 error trials were available after semi-automatic artefact rejection. We assessed ERPs at clusters of merged electrodes applying a region of interest approach –see Supplementary Materials, Fig. S1. A frontal cluster was used for assessing the ERN component, FMΘ, and delta oscillations; a parietal cluster for the Pe component. We extracted mean amplitudes to assess ERN (0- 100ms after button press) and Pe (200-400 ms after button press) components. Time windows and electrode locations for data extraction were chosen based on previous literature (e.g., van 7 Noordt and Segalowitz, 2012). Neural oscillations were calculated using complex Morlet wavelet decompositions (see Supplementary Material for details). FMΘ were extracted within a frequency range of 4.6-7.8 Hz. Delta oscillations were extracted within a frequency range of 1-2.9 Hz. Subsequently, power values (in µV2) were averaged condition- and participant-wise. Mean FMΘ and delta were extracted 0-100 ms after button press in the frontal electrode cluster (see also Supplementary Materials, section 12, for an additional delta analysis in the Pe time range).

2.8 Statistical analyses

All data were analysed using multilevel modelling (MLM). This was done for the current data to avoid losing participants for whom not all conditions were available, assuming that data were missing randomly. Five participants committed less than five error trials, which renders the assessment of error-related ERPs and oscillations unreliable (Amodio et al., 2004; Steele et al., 2016). Only correct trials were available for analysis in those participants. Neural data were modelled as a function of drug-group and correctness as fixed effects (see Supplementary Materials, section 9, for effect coding and more details). A random intercept and a random slope were estimated for each participant, restricted maximum likelihood estimations for fixed effects, and the Satterthwaite method for estimating degrees of freedom were applied.

Statistical analyses were performed using SPSS 22 (SPSS Inc., IBM Corporation, NY) and jamovi (The jamovi project, 2020). The significance level was set at p<.05. As effect size measure, we provide semi-partial R2 for MLM. Significant interactions in MLM were resolved with simple slope analysis testing for group differences first.

2.9 Behavioural data analysis

Reaction times were defined as the interval from middle letter offset until button press. Reaction times faster than 200ms were discarded (Hajcak et al., 2005; Wiswede et al.,

2009). Individual mean reaction times were calculated participant- and condition-wise and modelled as a function of drug-group and correctness or congruency as fixed effects Post- error slowing (PES; Rabbitt, 1966) was modelled with the fixed effect factors drug-group and sequence with reaction times of correct trials extracted participant-wise before and after incongruent erroneous trials (Dutilh et al., 2012). An alternative calculation of post-error slowing, as well as post-error accuracy and congruency-sequence-effects as further behavioural task measures can be found in Supplementary Materials, section 11. Error 8 frequency and missed responses were calculated participant-wise and analysed separately with robust t-tests. Welch’s t-tests were used to address group differences of normally- distributed questionnaire scores, robust t-tests for those lacking normal distribution. Error frequency and missed responses were calculated participant-wise and analysed separately with such robust t-tests. The affective ratings during the task were averaged together and analysed with robust t-tests as well.

3. Results

3.1 Behavioural results

The drug-groups did not differ significantly from each other regarding age, BMI, and questionnaire scores (Table 1). No group differences were observed for subjective drug experience (Supplementary Materials Table S1, DEQ-5). Of 15 BUP participants (data of one BUP participant are missing), 40% (n=6) believed that they were assigned to the BUP group, while this was the case for 26% (5 out of 19) control participants. The ratio of these

2 correct/incorrect self-assignments was comparable over the groups (χ (df=1)=0.72, p=0.40). More BUP than control participants reported dizziness, transpiration, muscular problems, and altered perception after the experiment (Supplementary Materials, Table S2).

Table 1. Sample descriptives and questionnaire data

BUP group Control group M SD M SD Welch's t df Age 24.06 2.79 23.79 2.95 -0.28 32.5 BMI 23.92 2.37 23.83 2.29 -0.11 31.6 BDI 6.13 4.90 5.17 4.51 0.59 30.7 STAI Trait 35.38 9.42 35.61 7.41 -0.08 28.4 PSWQ 36.00 12.65 42.22 9.51 -1.60 27.7 MDBF - T1: good/bad mood 18.13 1.92 17.68 2.29 0.62 31.8 MDBF - T1: awake/tired 16.60 2.92 15.63 3.48 0.88 31.9 MDBF - T1: calm/nervous 16.53 2.33 16.63 2.97 -0.11 32.0 MDBF - T2: good/bad mood 18.13 1.73 17.05 2.32 1.56 31.9 MDBF - T2: awake/tired 13.87 3.80 12.37 2.89 1.27 25.6 MDBF - T2: calm/nervous 16.87 3.11 16.63 2.36 0.24 25.5 Note: MDBF: BUP (n=15), Control (n=19); BDI, STAI, BSWQ: (BUP=16), Control (n=18); T1: time point 1; T2: time point 2

9 Reaction times were faster for erroneous compared to correct responses (main effect correctness: b=-26.95, SE=3.64, t(28.76)=7.40, p<.01, semi-partial R2=0.66). Drug-group and the interaction with correctness had no significant influence on reaction times (both p’s>.42). Reaction times were faster following congruent compared to incongruent trials (main effect congruency: b=24.88, SE=1.29, t(33)=19.24, p<.01, semi-partial R2=0.92). A significant interaction with drug-group was observed (b=-2.96, SE=1.29, t(33)=-2.29, p=.03, semi- partial R2=0.14). Simple slope tests did not show significant group differences (both p’s>.18). The interaction was driven by a larger RT difference between incongruent and congruent in BUP (M=55.69, SD=17.56) than in control participants (M=43.84, SD=13.00; both p’s<.01). A clear post-error slowing effect was observed (main effect sequence: b=10.79, SE=3.16, t(28)=3.41, p<.01, semi-partial R2=0.29), no other effects were found (both p’s>.76). Error numbers (p=.20) and the number of missed responses (p=.57) did not differ between the two groups. Similarly, affective ratings during the task were comparable for the two groups (both p’s>.14; Table 2), all participants reported to feel rather comfortable and not worried during the task.

Table 2. Behavioural and rating results

BUP group Control group M SD M SD Reaction times correct 435.56 54.15 419.64 44.64 error 385.46 78.11 369.01 56.74 congruent 405.81 59.12 393.91 45.97 incongruent 461.49 52.10 437.76 47.03 post-error 441.12 52.22 448.69 58.79 pre-error 420.57 57.28 426.09 66.55

Frequency Yuen's/p error numbers 11.94 7.15 23.63 23.17 0.20 missed 18.44 31.86 8.00 7.05 0.58 responses Ratings worry 2.16 1.13 2.35 2.13 0.63 comfort 2.84 1.75 2.40 1.40 0.50

3.2 EEG results

ERN amplitudes were more negative for errors than correct responses (main effect correctness: b=-2.99, SE=0.37, t(30.09)=-8.13, p<.01, semi-partial R2=0.69). The factor drug- 10 group and its interaction with correctness were not significant (both p’s>.13). For Pe amplitudes, the main effects of correctness (b=4.55, SE=0.43, t(31.79)=10.63, p<.01, semi- partial R2=0.78) and drug-group (b=1.38, SE=0.63, t(34.16)=2.17, p=.04, semi-partial R2=0.12), and their interaction (b=1.07, SE=0.43, t(31.79)=2.49, p=.02, semi-partial R2=0.16) were significant. Post-hoc analyses showed significant group differences for error trials (b=2.44, SE=0.79, t(57.34)=3.08, p<.01, semi-partial R2=0.14). Pe amplitudes following errors were more pronounced (i.e., more positive) in control than in BUP participants. No group differences occurred for correct trials (p=.68) –Figure 1, Table 3.

Figure 1. The upper left panel depicts grand average waveforms of error and correct amplitude courses at the frontal electrode cluster plotted for the buprenorphine (BUP; black) and the control group (CON; grey). Time point zero indicates participants’ button press. On the right-hand side, mean scalp topographies (in µV) of error and correct trials are depicted in the time window [0;100] ms after button press for both groups to demonstrate the ERN scalp distribution. The lower left panel depicts grand average waveforms of both groups for error and correct trials at the parietal electrode cluster. On its right-hand side, mean scalp topographies (in µV) are depicted in the time window [200;400] ms to demonstrate the Pe scalp distribution.

11 FMΘ oscillations in the ERN time frame (0-100ms post response) were larger for erroneous than correct responses (main effect correctness: b=1.71, SE=0.13, t(33.80)=13.01, p<.01, semi-partial R2=0.83). The main effect of drug-group and its interaction with correctness were not significant (both p’s>.32). Delta oscillations 0-100ms post response showed main effects of correctness (b=0.85, SE=0.15, t(33.90)=5.87, p<.01, semi-partial R2=0.50) and drug-group (b=0.46, SE=0.17, t(35.05)=2.65, p=.01, semi-partial R2=0.17), their interaction was not significant (p=.22). Early frontal delta was enhanced for erroneous than correct responses, and in control than BUP participants –Figure 2, Table 3. The same pattern of results emerged for delta oscillations in the Pe time range at the parietal electrode cluster (Fig. S2 in Supplementary Materials).

Figure 2. Panel (A) depicts delta oscillations scalp topography 0-100 ms after error commission averaged for error and correct trials over all participants (main effect correctness), and averaged for BUP and CONTROL participants over all trials (main effect drug-group). Panel (B) depicts the delta time course in the time window [-400; 600] for both groups and conditions. Panel (C) depicts time-frequency (TF) decomposition of the [error – correct] difference for all participants at the frontal cluster. The black rectangles denote frequency and time windows for delta oscillations analyses. Panel (D) depicts TF decomposition of the group difference [CONTROLS – BUP] at the frontal cluster.

XTable 3. EEG results

12 BUP group Control group M SD M SD ERN corre -0.91 3.11 1.68 3.52 ct error -6.24 4.03 -5.18 4.84 Pe corre -4.30 3.09 -3.68 4.21 ct error 2.65 4.92 7.42 4.96 FMΘ corre 0.26 0.51 0.63 0.63 ct error 3.94 1.66 3.79 1.40 Frontal delta corre 0.21 0.50 0.76 0.61 ct error 1.55 1.39 2.82 1.97

4. Discussion

This study tested whether acute administration of a partial µ-opioid receptor agonist/κ- opioid receptor antagonist affects behavioural and neural measures of error processing. While behaviour did not differ between the groups, we observed significantly decreased neural error signals in the opioid administration group in more elaborate processing stages following erroneous responses, and for delta oscillations in all trials.

Our primary ERP finding was a decrease in Pe amplitudes in response to errors in the BUP compared to the control group. While both automatic (ERN) and more elaborate (Pe) error processing clearly differentiated between erroneous and correct responses, only the latter component was significantly influenced by the administration of buprenorphine. Pe amplitude variation is often interpreted in light of conscious error awareness and attention towards errors (Nieuwenhuis et al., 2001), evidence accumulation that an error had occurred (Steinhauser and Yeung, 2010), and motivational significance of an error (Falkenstein et al., 2000) – thereby all referring to downstream processes bringing erroneous responses and their consequences to conscious awareness. All these interpretations suggest that more elaborate error processing was less pronounced in participants in the BUP group than in controls. In

13 contrast, early automatic error processing, reflected in ERN variation, was not significantly affected by opioid administration. Differences in Pe amplitudes are less frequently reported in studies on error processing than ERN differences. For example, Rodeback et al. (2020) observed that a stress induction primarily influenced Pe amplitudes. The authors interpreted this finding in light of lower awareness of errors while being stressed, possibly because the stress induction took away processing resources affecting more elaborate error processing. BUP participants reported slightly more side effects than control participants (Supplementary Materials Table S2 and section 14). These could be considered as stressors taking away processing resources from error awareness. However, ratings of perceived worry and comfort during the task did not show any group differences and indicated that all participants felt rather content during the experiment. Mood ratings after the task showed the same pattern, and neither did reaction times and error rates differ between groups. These online measures assessed during and directly after the task therefore suggest that the influence of post- experimentally reported side effects should not be over-interpreted here. Moreover, buprenorphine administration was reported to dampen the physiological stress response after a social stressor (Bershad et al., 2015). This would suggest rather decreased task-associated stress responses in the BUP compared to the control group than more experienced stress due to the reported side effects.

Pe amplitude variation is also often interpreted in terms of motivational significance and error appraisal. In line with this interpretation, two recent studies investigated social context effects on errors (de Bruijn et al., 2017; Pfabigan et al., 2020). Both studies observed decreased Pe amplitudes for other- compared to self-relevant errors in their control groups, which suggests that conscious error processing is malleable by cognitive processing biases. Relatedly, Larson et al. (2013) observed reduced Pe amplitudes in response to a brief mindfulness intervention, which aimed to focus participants’ attention to experiences in the presence in a non-judgemental way. Again, it was the conscious error appraisal that was affected by the experimental intervention, which aimed to marginalize the conscious – and potentially negative - experience of errors. Taken together, these findings suggest that BUP participants showed less conscious error appraisal than control participants.

Analysis of oscillatory brain activity corroborates the interpretation of the Pe findings. Both FMΘ and delta oscillations were enhanced for errors, thus replicating previous results of enhanced need for control after error commission (Cohen and van Gaal, 2014; Yordanova et al., 2004). However, BUP participants showed in general dampened delta responses to all 14 trials. Error-related delta oscillations are assumed to specifically reflect error monitoring processes (Cohen and Cavanagh, 2011), which would suggest that ongoing monitoring was reduced in BUP compared to control participants. Delta oscillations are also interpreted reflecting motivational task relevance and target stimulus salience (Knyazev, 2007, 2012). This would suggest that BUP participants were in general less engaged and less bothered whether they committed an error or not.

Both ERP and neural oscillation results suggest that errors – or task processing in general – were less salient during task performance in BUP participants. Our findings are thus in line with the hypothesis of dampening of negative affective arousal by opioids (van Steenbergen et al., 2019). Furthermore, they allow refining this hypothesis by showing effects rather during more controlled and conscious error processing stages (Pe, delta), but less so during the initial automatic and bottom-up detection of errors (ERN, FMΘ). Supporting our observation, a recent study aiming to reduce the assumed negative error-associated arousal by inducing positive affect observed diminished Pe amplitudes in the positive mood group, again interpreted as decreased appraisal of motivational error significance (Paul et al., 2017). While a positive mood induction might be a more powerful manipulation to decrease negative affect after error commission, the administration of opioids might work on similar pathways.

The current results of decreased error appraisal are also in line with animal and human studies ascribing buprenorphine the role of a mood regulator. This suggestion was initially brought up by opioid treatment studies, which observed that the administration of BUP also reduced depressive symptoms in individuals addicted to opioids (Bodkin et al., 1995; Kosten et al., 1990). More recent studies lend further support to this suggestion (Bershad et al., 2018; Fava et al., 2016; Ipser et al., 2013; Karp et al., 2014). For example, Bershad et al. (2018) showed that BUP decreased attention towards fearful faces, an effect that was particularly pronounced in individuals with higher scores on a state measure of negative affectivity. Along these lines, also animal research showed that BUP could lead to a decrease in depression- and anxiety-like behaviours (Falcon et al., 2015; Robinson et al., 2019). Importantly, the current participants were specifically recruited to have low levels of depressive and anxiety-related symptoms in the weeks prior to the experiment, and the two experimental groups were well matched in this regard. Thus, we are confident that the observed group effects were not caused by individual differences in state negative affectivity.

15 An important question refers to whether the hypothesised dampening of negative arousal related to errors (Dignath et al., 2020; Inzlicht et al., 2015) is linked to opioid administration or whether other neuropharmacological pathways might exert similar effects. Opioid receptors are co-localized with other monoaminergic neurotransmitters implicated in error processing and reward prediction errors (Fields and Margolis, 2015; Le Merrer et al., 2009; Valentino and Van Bockstaele, 2015). Targeting a related question, Randles et al. (2016) tested whether the non-opioidergic analgesic acetaminophen affects error processing, as a previous study had shown that acetaminophen caused a general blunting of evaluative processes (Durso et al., 2015). In line with our findings, Randles et al. (2016) observed a reduction of Pe amplitudes in the acetaminophen compared to the control group in their between-subjects, double-blind placebo-controlled study. This would suggest that analgesic substances with different mechanisms of action (acetaminophen acts on enzymes responsible for the production of prostaglandins; White, 2005) could impact error processing in a similar way. However, a recent replication attempt to clarify the effects of acetaminophen on neural correlates of error-, conflict-, and feedback processing failed to observe any significant effects of the non-opioid analgesic (Garrison et al., under review). Thus, also the current results await replication to allow a better understanding of the precise neural and neuropharmacological mechanisms underlying error processing.

In contrast to neural task measures, behavioural task measures such as error rates and reaction times were not significantly influenced by the administration of buprenorphine, which is in line with our previous study exploring error processing with opioid-related genetical variation (Pfabigan et al., 2015). Additional analyses focusing on adaptive control processes, however, showed that participants in the BUP group displayed rather inconsistent adaptive control following errors. They employed a slowing down after errors only in the first half of the experiment, but sped up after errors in the second half. Further, they were not sensitive to the presentation of incongruent trial arrays, as they did not slow down in the subsequent trial (see Supplementary Materials, section 11). Task difficulty of a Flanker task is rather low and the absence of significant group differences for reaction times and error rates might reflect ceiling-effects. Supporting this, Weinberg et al. (2012) suggested that error-related ERP variation is rarely accompanied by behavioural effects as individuals might be able to activate compensatory behavioural mechanisms when performing simple stimulus- response tasks (Miller, 1996). However, looking at more complex measures of adaptive control revealed the interfering effects of buprenorphine administration. The absence of

16 group differences for error rates prevents confounds in the electrophysiological results as they impact error-related amplitude variation (Fischer et al., 2017). In the current study, post-error slowing seemed to be rather orienting-related as all participants failed to translate slower responses following errors into higher accuracy rates after errors (Wessel, 2018).

Effects of buprenorphine on attentional and orienting processes could be another mechanisms explaining the observed group differences. We performed control analyses to account for possible effects, please refer to Supplementary Material, sections 15 (heart rate analysis) and 17 (attentional ERPs). However, results of these analyses provided no evidence for buprenorphine interfering with attentional processing.

A limitation of the current study is the fact that buprenorphine is a complex substance and acts not only as a partial agonist at the µ-opioid receptor, but also as an antagonist at the κ- opioid receptor. To further test the predictions made by van Steenbergen et al. (2019) and to clarify whether the observed effects are driven by µ-opioid or κ-opioid receptor activity or both, future studies should administer selective µ-receptor agonists such as morphine or hydromorphone, and selective κ-opioid receptor antagonists such as aticaprant (as used in Pizzagalli et al., 2020) in double-blind placebo-controlled designs to probe error processing, as well as the reward valuation part of the proposed framework. A better understanding of buprenorphine’s effects on cognition is particularly relevant as it is widely used in opioid- replacement therapies over long periods. As the current participants were drug-naïve and received buprenorphine only once, we cannot exclude that prolonged administration would have diverging effects from those reported here. Therefore, future studies should also investigate neural correlates of error processing in individuals under continuous opioid- replacement therapies. This could also contribute to the question why opioids have such a high addictive potential in a sub-group of individuals, and how to identify them. Future studies probing the predictions of van Steenbergen et al. (2019) should also assess self-report and psychophysiological markers of affective responses to errors and rewards, such as fEMG, skin conductance, startle responses, or pupil size, in combination with EEG and functional imaging methods.

Overall, our neural results suggest that buprenorphine as partial µ-receptor agonist/κ- receptor antagonist has the potential to dampen motivational error signals. This is of particular clinical relevance as buprenorphine is frequently used in opioid-replacement therapies in much higher doses than tested here.

17 Acknowledgements

We are thankful to Alexandra Karden and Josef Fehn for their support during participant recruitment and data collection. Parts of the present data were presented at the 60th

Annual Meeting of the Society for Psychophysiological Research (SPR), which was held online in 2020. Hypotheses, study design, and analyses were not pre-registered.

Funding

This study acknowledges funding by the University of Vienna and the Austrian

Science Fund (FWF, P32686, to CL and MR). Open access funding was provided by the

University of Oslo. The funding sources had no role in study design, data collection, analysis, or interpretation of the current data. The authors declare no conflict of interest.

Contribution

DMP and CL designed the research. DMP, MR, and IR were involved in data collection. DMP analysed the data and wrote the first draft. Critical revision for important intellectual content was done by DMP, MR, SLK, IR, and CL. All authors gave final approval of the submitted version.

18 References

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22 SUPPLEMENTARY MATERIALS The administration of the opioid buprenorphine decreases motivational error signals

Daniela M. Pfabigan, Markus Rütgen, Sara L. Kroll, Riečanský, I. & Claus Lamm

Contact: [email protected]

This file contains additional methodological details as well as extended information on the two participant groups.

1. Power analysis A priori power analysis suggested a sample size of 34 participants to detect medium-sized effects in a mixed ANOVA design (f=0.25, α=.05, β=.80, correlation among repeated measures=.50; G*Power 3.1; (Faul et al., 2009). We expected between 10-20% of dropouts. We used an effect size of partial η2=.06 (which reflects a medium-sized effect common in psychological research (Richard et al., 2003) as basis for our power calculations. In our previous study, opioid effects on error processing were found to reflect a medium-to-large size effect, with partial η2=.13 (Pfabigan et al., 2015). However, to avoid over-estimation of such an effect, we chose a more cautious approach and used a medium-sized effect as basis. The value of .06 was converted by G*Power (Faul et al., 2009) into an f value of 0.25. The whole power calculation protocol is depicted below:

F tests - ANOVA: Repeated measures, within-between interaction Analysis: A priori: Compute required sample size Input: Effect size f = 0.2526456 α err prob = 0.05 Power (1-β err prob) = 0.8 Number of groups = 2 Number of measurements = 2 Corr among rep measures = 0.5 Nonsphericity correction ε = 1 Output: Noncentrality parameter λ = 8.6808527 Critical F = 4.1490974 Numerator df = 1.0000000 Denominator df = 32.0000000 Total sample size = 34 Actual power = 0.8151184

2. Further information on the substance buprenorphine Buprenorphine (BUP) is derived from thebain, a natural alkaloid of the opium poppy. It is a semi-synthetic opioid with a complex pharmacology (Lutfy and Cowan, 2004). BUP acts as a partial agonist at µ-opioid receptors (with a strong receptor affinity that is even higher than those of widely used µ-opioid receptor antagonists such as naloxone). It acts further as agonist at the nociceptin receptor, and as antagonist at the δ-opioid and the κ-opioid receptor.

23 BUP has a long history of use in treating opioid dependence (Robinson, 2006) and for pain management (Picard et al., 1997). Although BUP acts only as a partial agonist at µ-opioid receptors, its analgesic effects seem comparable to full µ-opioid receptor agonists in humans (Raffa et al., 2014). In addition, the partial agonism has the advantage that BUP shows a ceiling effect in terms of respiratory depression (Dahan et al., 2006; Davis, 2012; Lutfy and Cowan, 2004), thus BUP can be considered a safer option than other µ-opioid receptor agonists for pain treatment and opioid maintenance therapy. Further, BUP is associated with a limited physical dependence (Walsh and Eissenberg, 2003). In opioid substitution therapies, doses of several milligram are common, whereas sublingual tablets with a dose of 0.2 mg and 0.4 mg are regularly prescribed for moderate to severe chronic pain (Schmidt-Hansen et al., 2015; Zacny et al., 1997). BUP is mainly metabolized in the liver and excreted in the bile (Elkader and Sproule, 2005). It undergoes extensive first-pass metabolism, thus its low oral bioavailability. In contrast, with sublingual administration, BUP’s bioavailability is long enough to serve as important administration route for opioid substitution therapy (see Elkander and Sproule, 2005, for details). Sublingual administration results in peak plasma levels about 90-360 minutes after administration (Mendelson et al., 1997). Due to its complex pharmacology, BUP effects could be attributed to either it being a partial agonist at the µ-opioid receptor, or it being an antagonist at the κ-opioid receptor. Effects on the δ-opioid receptor seem to play less of a role as BUP displays a lower affinity for this receptor as for µ- and κ-opioid receptors (summarized by Lutfy and Cowan, 2004). While research on µ-opioid receptors is manifold, less is known about the functional significance of κ-opioid receptors. They have been suggested as promising targets in the clinical setting treating stress-related disorders (for an overview, see Jacobson et al., 2020). Evidence for this claim comes from animal research demonstrating that the administration of κ-opioid receptor antagonists resulted in less stress-induced immobility (McLaughlin et al., 2006) and a decrease of depressive-like behaviours in an animal model of depression (i.e., in a learned helplessness; Shirayama et al., 2004) in rodents (Falcon et al., 2015; Robinson et al., 2019). In contrast, the administration of an agonist at the κ-opioid receptor resulted in depressive- like behaviours in rats (Carlezon et al., 2006). There is further evidence that agonists and antagonists of the κ-opioid receptor have diverging effects on subsequent dopamine release when injected in the ventral striatum (Maisonneuve et al., 1994), which further provides functional explanations of how changes at the κ-opioid receptor exert their influence on behaviour via other neurotransmitter systems. In human research, first hints for this claim came from studies that investigated how BUP fared in opioid substitution therapies. The authors observed reports that BUP reduced depressive symptomatology in individuals suffering from an opioid addiction (Bodkin et al., 1995; Kosten et al., 1990). A recent clinical trial administering a κ-opioid receptor antagonist vs. placebo observed that after 8 weeks of antagonist treatment, participants showed improvements in self-reported anhedonia, which was supported by behavioural and neural findings. However, self-reported depression and anxiety states were not influenced by κ-opioid receptor antagonism (Krystal et al., 2020). Another line of research suggests that κ-opioid receptors are implicated in the pathogenesis of multiple sclerosis (Du et al., 2016). Recent efforts in identifying a reliable radio ligand to

24 map κ-opioid receptors via positron emission tomography seem promising (Tangherlini et al., 2020). This will boost future research and enhance our understanding of their function fundamentally. As also stated in the main document, it is not clear whether the observed effects of BUP on neural correlates of error processing are caused by µ-opioid receptor or κ-opioid receptor activity, or a combination of both. To further test the predictions made by van Steenbergen et al. (2019), future studies should administer selective µ-receptor agonists such as morphine or hydromorphone, and selective κ-opioid receptor antagonists such as aticaprant (as used in Pizzagalli et al., 2020) in double-blind placebo-controlled within-subject designs to probe error processing, as well as the reward valuation part of the proposed framework. These studies should further assess not only neural correlates of error processing, but also self- report and psychophysiological markers of affective responses to errors and rewards, such as fEMG, skin conductance, startle responses, or pupil size. This will allow for a more differentiated investigation of the underlying mechanisms that drive BUP’s effects.

3. Drug Effects Questionnaire DEQ-5 (Morean et al., 2013)

Table S1. DEQ-5 results BUP Controls Yuen's test M SD M SD t df p Do you feel a drug effect right FEEL 24.08 28.54 12.49 18.38 0.85 9.38 0.416 now? Are you high right now? HIGH 17.09 25.66 2.18 3.37 1.14 8.13 0.287

Do you dislike any of the DISLIKE 17.05 24.71 8.08 9.87 0.34 15.44 0.736 effects you are feeling right now? Do you like any of the effects LIKE 16.06 24.36 14.07 22.24 0.12 16.77 0.909 you are feeling right now? Would you like more of the MORE 1.89 2.02 6.90 22.76 0.60 17.10 0.554 drug you took, right now?

4. Side effects

Table S2. Side effects BUP (n=15) Controls (n=19) %yes yes no %yes yes no χ p Daze 13.3 2 13 10.5 2 17 0.06 0.801 Fatigue 53.3 8 7 52.6 10 9 1.45 0.228 Need for sleep 26.7 4 11 36.8 7 12 0.40 0.529 Weakness 20 3 12 10.5 2 17 0.60 0.439 Dizziness 53.3 8 7 5.3 1 18 9.95 0.002 25 Tinnitus 6.7 1 14 0 0 19 1.31 0.253 Heaviness stomach 6.7 1 14 0 0 19 1.31 0.253 Nausea 6.7 1 14 0 0 19 1.31 0.253 Vomiting 0 0 15 0 0 19 Dry mouth 6.7 1 14 0 0 19 1.31 0.253 Transpiration 26.7 4 11 0 0 19 5.74 0.017 Disorientation 20 3 12 5.3 1 18 1.75 0.185 Nervousness 6.7 1 14 10.5 2 17 0.16 0.694 Head ache 0 0 15 0 0 19 Itching 0 0 15 10.5 2 17 1.68 0.195 Anxiety state 0 0 15 0 0 19 Euphoria 6.7 1 14 0 0 19 1.31 0.253 Depressive mood 0 0 15 0 0 19 Palpitation 0 0 15 0 0 19 Tingling sensation in 20 3 12 10.5 2 17 0.60 0.439 fingers and toes Impaired vision 6.7 1 14 5.3 1 18 0.03 0.863 Respiratory problems 6.7 1 14 5.3 1 18 0.03 0.863 Problems with urination 0 0 15 0 0 19 Diarrhea 0 0 15 0 0 19 Muscular problems 20 3 12 0 0 19 4.17 0.041 Altered perception 33.3 5 10 5.3 1 18 4.55 0.033 Others 0 0 15 0 0 19

26 5. Addition participant characteristics regarding previous substance use

Table S3. Substance use characteristics BUP group (n=16) Control group (n=19) More than 6 More than 6 Current use Never Current use Never months ago months ago % n % n % n % n % n % n Alcohol 93.8 15 0 0 6.3 1 78.9 15 10.5 2 10.5 2 Cigarettes 18.8 3 50 8 31.3 5 31.6 6 42.1 8 26.3 5 Tobacco use 6.3 1 37.5 6 56.3 9 5.3 1 47.4 9 47.4 9 Cannabis 6.3 1 31.3 5 62.5 10 21.1 4 38.8 7 42.1 8 Amphetamines 0 0 0 0 100 16 0 0 5.3 1 94.7 18 Cocaine 0 0 0 0 100 16 0 0 5.3 1 94.7 18 MDMA 0 0 0 0 100 16 0 0 5.3 1 94.7 18 Opiates 0 0 0 0 100 16 0 0 0 0 100 19 Hallocinogenics 0 0 0 0 100 16 0 0 5.3 1 94.7 18 Ritalin/Methylphenidate 0 0 0 0 100 16 0 0 0 0 100 19 GHB and others 0 0 0 0 100 16 0 0 0 0 100 19

27 Pfabigan, D.M.

6. Additional information on participants and study design After taking either the BUP or the placebo pill, participants completed two tasks assessing empathic reactions and error processing. This report focuses on the error-processing task. Participants’ handedness was assessed via a questionnaire (Oldfield, 1971). Initially, we recruited 42 volunteers. Screening consisted of an online questionnaire and a subsequent phone interview asking for physical and psychological health and self-reported drug-use history. Inclusion criteria consisted of age between 20 and 40, right-handedness, fluency in German, BMI of 19-26, no current or past psychiatric disorder, no current drug use in the last two weeks, and no current intake of any prescribed pharmaceuticals. One participant did not show up, data of six further participants were not collected because they were reporting nausea after taking the pill (n=5) or had elevated blood pressure (n=1). Participants were further required to refrain from recreational drug use and drinking grapefruit juice (Pirmohamed, 2013) within 24 hours before the test session (concerning drug use, this was verified by a urine sample taken right after participants signed the informed consent). They were asked to have a good night’s sleep before the test session and come satiated to the laboratory. Of 15 BUP participants, twelve were invited before lunchtime (between 9 and 10 am), and three participants after lunch time (between 1 and 1.30 pm). Of 19 control participants, eleven were invited before lunchtime, and eight afterwards. All participants who came to the experimental session received a financial compensation at the end of the experiment (€40). During the online screening, participants were also asked to report whether they are regularly consuming coffee, energy drinks, and other caffeinated beverages (see Table S4). This was done because substances such as alcohol, tea, coffee, and energy drinks influence enzyme activity that is implicated in the metabolic pathway of buprenorphine (Hagelberg et al., 2016; Iribarne et al., 1997). Importantly, no group differences were observed here for these regular habits (contingency tables, all p’s>.12).

Table S4. Use of caffeinated drinks BUP group (n=15) Daily/almost Less than 1-3 times/week Never daily once/week % n % n % n % n Coffee 26.7 4 13.3 2 13.3 2 46.7 7 Energy drinks 0 0 0 0 20.0 3 80.0 12 Other caffeinated drinks 13.3 2 20 3 13.3 2 53.3 8

Control group (n=19) Daily/almost Less than 1-3 times/week Never daily once/week % n % n % n % n Coffee 15,8 3 10.5 2 36.8 7 36.8 7 Energy drinks 0 0 5.3 1 42.1 8 52.6 10 Other caffeinated drinks 0 0 5.3 1 36.8 7 57.9 11

28 Pfabigan, D.M.

The current dose of 0.2mg BUP is considered a rather low dose (Zacny et al., 1997), and was chosen in accordance with previous studies (Bershad et al., 2018; Bershad et al., 2016), which had reported only marginal side effects or nausea.

7. Additional information on the experimental procedure and task The physician (IR) provided the information that either an approved and effective painkiller is administered or a placebo pill without any effects. During EEG preparation, participants watched a nature documentary. Inside the EEG chamber, participants’ heads were positioned in a chin rest to minimize head movements to avoid nausea. The current error processing task took place approximately 75-105 minutes after drug/placebo administration. A modified version of the Eriksen flanker task (Eriksen and Eriksen, 1974) was used to assess error processing. Participants had to indicate target letters (H, S, K, or C) in the middle of a five-letter string via button press. Keys 1 and 3 on the number pad of a standard German- language QWERTZ keyboard were assigned to the target letters and had to be pressed with the index and the middle finger of the right hand (counter-balanced assignment across participants). The white letter strings were either congruent (e.g., HHHHH, SSSSS) or incongruent (e.g., HHSHH, SSCSS) and presented centrally on black background. Each trial started with the presentation of a fixation cross for 200ms, followed by the four outer letters for 100ms. Next, the central letter was blended in this array for 35ms (Kopp et al., 1996). Afterwards, participants had 870ms to give a response while a black screen was presented. Performance feedback was only presented in case participants responded too slow, demanding faster responses (duration 1000ms), otherwise a fixation cross was blended in for 1000ms, followed by an inter-stimulus interval of 800-1200ms also showing the fixation cross. After 24 training trials, participants performed 304 trials with 50% congruent and 50% incongruent stimuli, presented randomly. Participants were given short, self-paced breaks every 50 trials. Before each break, participants had to give affective ratings addressing how worried and comfortable they felt during the last trials on 9-point Likert scales ranging from “not worried/comfortable” (1) to “worried/uncomfortable” (9). At the very end of the experiment, participants were asked whether they had experienced side effects of the drug by providing them with a list of possible symptoms (based on the instruction leaflet of Temgesic®). They could indicate with “yes” or “no” whether they had experienced these side effects or not (see Table S2).

8. Detailed description of the electrophysiological recordings and data analysis EEG measurements were performed in an electromagnetically shielded chamber. Stimulus presentation was controlled by E-Prime 2.0 (Psychology Software Tools, Inc., Sharpsburg, PA). Stimuli were presented on a 19 inch CRT monitors (refresh rate 85Hz). EEG was recorded with 59 Ag/AgCl electrodes embedded equidistantly in an elastic electrode cap (EASYCAP GmbH, Herrsching, Germany, model M10). Vertical and horizontal electro- oculogram (EOG) was recorded via four additional electrodes. A ground electrode was placed in the middle of the forehead serving as online reference. Two additional electrodes

29 Pfabigan, D.M. were placed on participants’ right collarbone and below the costal arch on their left body half for collection of electrocardiogram data (ECG). Electrode impedances were kept below 3 kΩ by applying a skin-scratching procedure (Picton and Hillyard, 1972) and using degassed electrode gel (Electrode-Cap International, Inc., Eaton, OH). All electrophysiological signals were recorded with a DC amplifier set-up (NeuroPrax, neuroConn GmbH, Ilmenau, Germany) from DC to 250 Hz and sampled at 500 Hz for digital storage. The flanker task lasted about 20 minutes. EEG data were analysed using EEGLAB 13_1_1b (Delorme and Makeig, 2004) in Matlab 2014a. Offline, high pass (0.1 Hz) and low-pass filters (cut-off frequency 30 Hz, roll-off 6dB/ octave) were applied to continuous data. Then, data were re-referenced to the averaged signal of the mastoid channels, segmented into epochs [-1000;2000] ms around the onset of the outer flankers, and extended infomax independent component analysis (ICA; Bell and Sejnowski, 1995) was applied to detect eye movement-related artefacts. After discarding them, response-locked data segments were extracted starting 400 ms prior to the button press and lasting for 1200 ms (correct, error). The interval [-200;-100] ms prior to button press served as baseline interval. It was chosen because of its distance to the motoric response (see, for example, Good et al., 2015; Rigoni et al., 2015). Subsequently, semi-automatic artefact correction was conducted in EEGLAB. Trials with voltage values exceeding +/- 75µV (pop_eegthres) or voltage drifts > 50µV (pop_eegrejtrend) were automatically marked by the algorithms. These trials were rejected in case visual inspection also indicated artefact affliction. On average, 221.43 (SD=42.95) correct trials and 17.67 (SD=15.19) error trials were available after artefact rejection (no group differences, both p’s > 0.36, robust independent t-tests). To keep the statistical model simple and to increase ERP signal-to-noise ratio (Luck and Gaspelin, 2017), we assessed ERPs at clusters of several merged electrodes applying a region of interest approach. An electrode cluster including FCz and six surrounding electrodes (R11, R14 [FCz], R15, L8 [Fz], L9, L12, L16 [Cz]) was used for assessing the ERN component and a cluster including Pz and five surrounding electrodes (R24 [CPz], R25, R29, L22, L27) for the Pe component. The number of electrodes chosen for the clusters was determined by the electrode layout of the EEG cap. Electrode FCz is surrounded by six electrodes, while Pz is surrounded by five electrodes when using the equidistant electrode layout of model M10 – see www.easycap.de and Figure S1 below.

30 Pfabigan, D.M.

Figure S1. Electrode clusters centred on FCz (in blue) capturing ERN, FMΘ, and delta variation, and centred around Pz (in red) capturing Pe variation.

We extracted mean amplitudes in the interval 0-100 ms with respect to the button press in the frontal cluster to assess ERN variation. For the Pe component, we assessed mean amplitudes in the parietal cluster in the time window 200-400 ms after button press. Time windows for data extraction were chosen based on previous literature (e.g., van Noordt and Segalowitz, 2012). For time-frequency analyses, pre-processing steps were exactly the same apart from applying a 40Hz low pass filter (roll-off 6dB/octave), initially extracting epochs with a length of [-2000;3500] ms around the onset of the outer flankers, and afterwards creating response- locked epochs with a length of [-2000;2000] ms to prevent edge effects. Similar artefact correction criteria were applied as for the ERP analysis. We calculated event-related spectral perturbations (ERSPs) trial- and electrode-wise per participant of artefact-free trials using complex Morlet wavelet decompositions implemented in the EEGLAB newtimef() function. For FMΘ, the artefact-free data were subjected to a Laplacian transformation using the CSD Toolbox (Version 1.1) [http://psychophysiology.cpmc.columbia.edu/Software/CSDtoolbox] to improve spatial resolution for localizing and quantifying the theta frequency band (Kayser and Tenke, 2006; Tenke and Kayser, 2012). ERSPs were calculated from 2 to 20 Hz in 29 bins (zero-padded 3-cycle Morlet wavelets were used for the lowest frequency, increasing linearly by 0.5 cycles until the highest frequency). FMΘ were extracted within a frequency range of 4.6-7.8Hz. Further, mean power with the time interval [-400;-200] ms prior to the response served as baseline interval. This interval was chosen to avoid interference of windowing post-response activity and padding values together, which could yield biased ERSP estimates. For delta oscillations, the original artefact-free trials were used and ERSPs were calculated from 1-10 Hz in 15 bins (zero-padded 2-cycle Morlet wavelets, linearly increasing by 0.5 cycles). The same baseline time window was applied. Delta oscillations 31 Pfabigan, D.M. were extracted within a frequency range of 1-2.9 Hz. Subsequently, power values (in µV2) were averaged condition- and participant-wise. In line with previous studies, pronounced FMΘ activity was observed at fronto-central electrode sites and pronounced delta activity at frontal and centro-parietal sites (Cavanagh and Frank, 2014; Cavanagh et al., 2012). Keeping our analysis strategies as comparable as possible, we used the same electrode cluster as for ERP analyses. Mean FMΘ and delta responses were extracted from button press to 100 ms afterwards at the FCz cluster.

9. Additional information on the statistical analyses Subsequently, a winsorization procedure was applied per ERP and ERSP because of different outlier rates per group (Wilcox, 2012). This procedure was applied to participants mean neural responses and mean reaction times for correct and incorrect trials per group to account for outliers before further statistical analyses was conducted. Including outlier values in the analysis violates assumptions of general linear model estimations (Keselman et al., 2003; Wilcox, 2010). Therefore, as suggested by Wilcox (2012), mean values higher than the 75th percentile plus 1.5 times the interquartile range, and mean values lower than the 25th percentile minus 1.5 times the interquartile range of the correct/incorrect trials were replaced with the maximum or the minimum value in the corresponding condition. ERPs were analysed using multilevel modelling (MLM) using the MIXED function in SPSS. This was done for the current data to avoid losing participants for whom not all conditions were available (de Bruijn et al., 2017; Saunders et al., 2018), assuming that data were missing randomly (Spronk et al., 2016). An MCAR test supports the assumption that all data were 2 missing randomly (χ (1, N=35)=0.137, p=.71). Five participants committed less than five error trials, which renders ERN assessment unreliable (Amodio et al., 2004; Pfabigan et al., 2013). Only correct trials were available for analysis in those participants (four in the BUP group, one in the control group). To be consistent throughout analyses, MLM was also applied to behavioural data. ERN and Pe amplitudes, and FMΘ and delta oscillations were modelled as a function of drug-group and correctness as fixed effects (effect-coded: control group: 1; buprenorphine group: -1; correct: -1; error: 1). A 2-level multilevel model was used accounting for correctness and drug-group nested within participants by estimating a random intercept and a random slope for each participant. We applied a restricted maximum likelihood estimations for the fixed effects, and the Satterthwaite method for estimating degrees of freedom. As effect size measures, we provide semi-partial R2 (Edwards et al., 2008) for MLM. Significant interactions in MLM were resolved with simple slope analysis testing for group differences first (Aiken and West, 1991). Regarding the effect size measure for MLM, semi- partial R2 (Edwards et al., 2008), values of 0.02, 0.13, and 0.26 denote small, medium, and large effects, respectively (Cohen, 1992).

10. Additional information on the behavioural data analysis

32 Pfabigan, D.M.

Reaction times were defined as the interval from middle letter offset until button press. Reaction times faster than 200ms were considered as anticipatory reactions and discarded (Hajcak et al., 2005; Wiswede et al., 2009). Individual mean reaction times were calculated participant- and condition-wise and modelled as a function of drug-group and correctness or congruency as fixed effects (effect-coded: control group: 1; buprenorphine group: -1; correct: -1; error: 1; congruent: -1; incongruent: 1; applying the same criteria as for the EEG data). Post-error slowing (PES; Rabbitt, 1966) was modelled with the factors drug-group and sequence (effect-coded: pre-error: -1; post-error: 1) with reaction times of correct incongruent trials extracted participant-wise before and after erroneous trials in 5-trial sequences where errors were preceded and followed by two correct responses (Dutilh et al., 2012). Error frequency and missed responses were calculated per participant and analysed separately with robust t-tests (Yuen’s test) as they were not normally distributed. Welch’s t-tests were used to address group differences of normally-distributed questionnaire scores, robust t-tests for those lacking normal distribution. The affective ratings during the task were averaged together and analysed with robust t-tests as they were not normally distributed either.

11. Additional behavioural analyses The following analyses were conducted in a post-hoc fashion as they were inspired by helpful reviewer comments during the revision process – see Table S5 for means and SD of all measures. Post-error accuracy (PEA): Post-error accuracy is assessing whether participants apply successful adaptive behavioural control following errors or not. It is calculated as the difference between post-error accuracy and post-correct accuracy (ΔPEA). Thus positive values of ΔPEA indicated enhanced behavioural adaptation following errors. We calculated PEA here in reference to (Schroder et al., 2020). Post-error and post-correct accuracy was assessed only following incongruent trials to avoid any effects attributed to stimulus congruency. Further, trials following missed responses were excluded as well. Participants with less than five errors were further excluded from this analysis. We have transformed the absolute ΔPEA value into a percent value. Since the data were normally distributed, a Welch’s t-test was used for group comparison. We observed no significant group differences for ΔPEA: t(18.86)=-0.96, p=.34. Percent values were negative in both groups (BUP group: M=-18.88%, SD=22.45; control group: M=-11.61%, SD=16.24) suggesting that errors did not lead to favourable adaptive changes in behaviour in the current experimental set-up. Please note that one control participant was not available for this analysis as he committed only errors in congruent trials or missed responses.

Classical post-error calculation (Rabbitt, 1966): In the classical calculation of post-error slowing, reaction times are compared for trials following errors and trials following correct responses. We followed the calculations described in (Schroder et al., 2020) and extracted reaction times following errors and correct responses only for incongruent trials, we excluded missed response trials, and we excluded double error trials (i.e., trials in which an error was followed by another error). Post-error trials were excluded if less than five errors were committed, while post-correct trials of these participants were included in the statistical

33 Pfabigan, D.M. model. This classical method for PES calculation is most likely biased by the fact that errors are not equally distributed over the course of the experiment. Therefore, we first added experimental block (the current experiment consisted of six blocks) as additional factor in our multilevel modelling. Reaction times were modelled with drug-group, pervious-trial- correctness, and mean-centred block as fixed factors (effect-coded: control group: 1; buprenorphine group: -1; correct: -1; error: 1). Better model fit was achieved when estimating a random slope for mean-centred block. This model resulted in significant main effects of previous-trial-accuracy (b=-13.73, SE=5.45, t(296.39)=-2.52, p=.01, semi-partial R2=0.02) and mean-centred block (b=-13.96, SE=2.82, t(95.98)=-4.95, p<.01, semi-partial R2=0.20). The drug-group x mean-centred block interaction (b=9.17, SE=2.82, t(95.98)=3.25, p<.01, semi-partial R2=0.10) and the previous- trial-accuracy x mean-centred block interaction (b=9.77, SE=3.19, t(312.30)=3.06, p<.01, semi-partial R2=0.03) were significant. Also the three-way interaction drug-group x previous- trial-accuracy x mean-centred block was significant (b=-10.65, SE=3.19, t(312.30)=-3.34, p=.001, semi-partial R2=0.03). To simplify the model and the interpretation of the three-way interaction, we merged the blocks per experimental half and modelled reaction times by drug-group, previous-trial- correctness, and experimental half (effect-coded: half 1: -1, half 2: 1). This model showed significant main effects of previous-trial-accuracy (b=6.03, SE=3.02, t(89.53)=2.00, p=.05, semi-partial R2=0.04) and experimental half (b=-14.26, SE=2.91, t(86.94)=-4.93, p<.01, semi-partial R2=0.22). The drug-group x experimental block interaction (b=11.17, SE=2.91, t(86.94)=3.84, p<.01, semi-partial R2=0.15) and the previous-trial-accuracy x experimental block interaction (b=7.09, SE=2.91, t(86.94)=-2.44, p=.02, semi-partial R2=0.06) were significant. Also the three-way interaction drug-group x previous-trial-accuracy x experimental block was significant (b=12.94, SE=2.91, t(86.94)=4.44, p<.01, semi-partial R2=0.19). Next, we separately calculated drug-group x previous-trial-accuracy models per experimental half. For half 1, we found a significant main effect of previous-trial-accuracy (b=12.37, SE=3.58, t(28.09)=3.46, p<.01, semi-partial R2=0.30). The main effect of group was not significant (b=-16.88, SE=9.54, t(28.09)=-1.77, p=.09), while the interaction was significant (b=-10.66, SE=3.58, t(28.09)=-2.98, p<.01, semi-partial R2=0.24). Testing for group differences, we found no significant reaction time differences for correct trials following correct trials (p>.53). Regarding correct trials following error trials, we observed significant group differences (b=-27.54, SE=10.44, t(44.83)=-2.64, p=.01, semi-partial R2=0.13). Participants in the BUP group had slower reaction times than participants in the control group. In half two, only the drug-group x previous-trial-accuracy interaction was significant (b=14.35, SE=3.83, t(28.20)=3.75, p<.01, semi-partial R2=0.33). Testing for group differences, none were observed for correct trials following correct trials (p>.30), and only a trend result for correct trials following error trials (b=18.95, SE=9.86, t(47.18)=1.92, p=.06). In contrast to half 1, now participants in the BUP group showed by trend faster reaction times than participants in the control group. So participants in the BUP group showed less post- error slowing during the second half of the experiment, while participants in the control group showed the opposite pattern.

Congruency-sequence effects (CSE):

34 Pfabigan, D.M.

These analyses were conducted mirroring the ones reported in van Steenbergen et al. (2017). Congruency-sequence-effects (CSE) reflect another form of behavioural adjustments that are related to successful behavioural control (Botvinick et al., 2001), namely a smaller reaction time difference between congruent and incongruent trials following incongruent in comparison to congruent trials (Gratton et al., 1992). This is assumed to reflect an increased attentional focus after incongruent trials leading to a correct response. We extracted mean response times of correct responses of congruent trials that were preceded by congruent trials (cC: previous trial congruent/current trial congruent) and by incongruent trials (iC: previous trial incongruent/current trial congruent), as well as mean response times of correct responses of incongruent trials that were preceded by congruent trials (cI: previous trial incongruent/current trial congruent) and by incongruent trials (iI: previous trial incongruent/current trial incongruent). Responses faster than 200 ms were discarded, as were the first trials after each block. In the LMM we modelled drug-group, previous-trial- congruency, and current-trial-congruency (effect-coded: control group: 1; buprenorphine group: -1; previous-trial-congruent: -1; previous-trial-incongruent: 1; current-trial-congruent: -1; previous-trial-incongruent: 1). The LMM showed significant main effects of previous-trial-congruency (b=2.76, SE=1.15, t(99)=2.40, p=.02, semi-partial R2=0.06) and current-trial-congruency (b=28.17, SE=1.15, t(99)=24.49, p<.01, semi-partial R2=0.86). Furthermore, the previous-trial-congruency x current-trial-congruency (b=-3.69, SE=1.15, t(99)=-3.21, p<.01, semi-partial R2=0.09) and the drug-group x previous-trial-congruency interaction (b=2.35, SE=1.15, t(99)=2.04, p=.04, semi-partial R2=0.04) were significant. No other effects were observed (all p’s>.17). The previous-trial-congruency x current-trial-congruency interaction reflects the conflict adaptation effect described with the following formula: (cI – cC) – (iI – iC) with M=15.31 ms, SD=21.39, in the current experiment. Simple slope analyses of the drug-group x previous-trial-congruency interaction showed no significant group differences (both p’s >.25). Control participants were significantly influenced by previous-trial-congruency (b=5.12, SE=1.49, t(33)=3.42, p<.01, semi-partial R2=0.26), their reaction times slowed down when the previous trial was incongruent. In contrast, no such slowing down was observed in participants in the BUP group (p=.80). Congruency-sequence-effects can also be calculated for accuracy data. Missed trials and the first trial of each block were discarded. We calculated a LMM analogue to the reaction time CSE model. However, we observed no significant effects. The previous-trial-congruency x current-trial-congruency interaction approached significance (p=.06), while all other p’s>.18.

Table S5. Descriptives of adaptive behavioural task measures

BUP group Control group M SD M SD PES classical half1-correct 436.21 52.65 423.77 51.17 half1-error 491.91 74.95 430.11 59.39 half2-correct 425.22 49.27 405.70 50.93 half2-error 397.81 60.14 434.95 60.11

CSE (RTs)

35 Pfabigan, D.M.

conCon 402.19 63.20 382.33 49.74 conIncon 465.09 50.58 446.90 40.23 inconCon 407.23 55.18 403.12 45.25 inconIncon 461.72 43.88 446.56 43.55 Conflict adaptation 8.41 20.04 21.13 21.25

CSE (ACC in total numbers) conCon 63.75 14.74 65.47 11.08 conIncon 67.19 11.72 65.84 7.08 inconCon 66.69 12.77 67.26 6.33 inconIncon 63.75 14.62 66.58 8.73 Conflict adaptation 6.38 18.72 1.05 18.87

12. Additional time frequency analysis at the posterior electrode cluster

In addition to the frontal delta analysis provided in the main document, we conducted exploratory delta analysis in the Pe timeframe at the posterior electrode cluster because Figure 2 in the main document suggested heightened delta activation during the Pe timeframe.

Delta oscillations in the Pe timeframe (200-400ms post response) at the posterior electrode cluster were significantly moderated by the factors correctness (b=1.09, SE=0.19, t(33.48)=5.70, p<.01, semi-partial R2=0.49) and group (b=0.52, SE=0.23, t(34.80)=2.21, p=.03, semi-partial R2=0.12). The interaction was not significant (p=.69). Later posterior delta was enhanced for errors compared to correct responses, and in control compared to BUP participants, mirroring the effects of frontal delta oscillations – see Table S6 and Figure S2. It would be extremely interesting to investigate whether later delta oscillatory measures (as assessed here) are also related to zygomaticus activity after error commission (see Dignath et al., 2019), as some research suggests that oscillations in this frequency band are associated with affective and motivational stimulus relevance (Bernat et al., 2015; Knyazev, 2007, 2012).

Table S6. Mean posterior delta band oscillations

BUP group Control group M SD M SD Posterior delta (200-400ms) correct -1.27 1.18 -0.39 1.16 error 0.77 1.61 1.94 2.46

36 Pfabigan, D.M.

Figure S2. Panel (A) depicts delta oscillations scalp topography 200-400 ms after error commission averaged for error and correct trials, and for BUP and CONTROL participants. Panel (B) depicts the delta time course in the time window [-400; 600] for both groups and conditions at electrode cluster around Pz. Panel (C) depicts time-frequency (TF) decomposition of the [error – correct] difference for all participants at the Pz electrode cluster. The black rectangles denote frequency and time windows for delta oscillations analyses. Panel (D) depicts TF decomposition of the group difference [CONTROLS – BUP].

To complete the results report of the main document, we have created an analogue plot for frontal midline theta (FMΘ) oscillations:

37 Pfabigan, D.M.

Figure S3. Panel (A) depicts frontal midline theta oscillations scalp topography 0-100 ms after error commission averaged for error and correct trials for all participants. Panel (B) depicts FMΘ time course in the time window [-400; 600] for both groups and conditions at the electrode cluster around FCz. Panel (C) depicts time-frequency (TF) decomposition of error and correct trials for all participants at the FCz electrode cluster. The black rectangles denote frequency and time windows for FMΘ oscillations analyses.

13. Exploratory correlation analysis EEG-behaviour Exploring the link between neuronal and behavioural task measures over all participants, we calculated Spearman correlations between reaction times on error trials, numbers of errors, and post-error slowing (deltaPES - difference in reaction times between pre-error and post- error trials) and each neuronal correlate assessed for error trials (ERN and Pe amplitudes, FMΘ and delta oscillations, 30 participants were available for these calculations). We applied Bonferroni correction to account for multiple testing per behavioural measures (0.05/4, pcorr<.0125). Reaction times on error trials were significantly correlated with Pe error trials

(rs=-.505, p=.004), shorter response times led to more pronounced Pe amplitudes. No other effects were observed (all p’s>.07). No significant results were observed for error numbers (all p’s>.02). For post-error slowing, we observed a significant positive correlation of deltaPES and FMΘ in error trials (rs=.514, p=.004). Larger post-error slowing was associated with more pronounced FMΘ, no other correlations with deltaPES were significant (all p’s>.13). We further explored whether the neuronal measures were associated with the subjective ratings at the end of the experiment. Again, Bonferroni correction was applied per rating

(0.05/4, pcorr <.0125). Here, we observed no significant associations for the worry rating (all p’s>.025) and the comfortable rating (all p’s>.027).

14. Additional correlation analyses regarding the occurrence of side effects The assessed occurrence of side effects after the EEG test session showed significant group differences in four categories. Therefore, we further explored the link between neuronal task measures and the occurrence of these respective side effects over all participants. We calculated point-biseral correlations between all neural measures and the occurrence (dummy-coded with 1) or absence (dummy-coded with 0) of side effects. For error trials, 29 participants were available. For correct trials, 35 participants were available. We applied Bonferroni correction to account for multiple testing per assessed side effect category for all neural measures (0.05/8, pcorr<.006). For dizziness, no correlation coefficients were observed below the significance threshold (Pe error trials: r=-.488, p=.007; delta correct: r=-.388, p=.023, all other p’s>.107). The same applied for transpiration (all p’s>.174) and muscular problems (p’s>.038). For the side effect category altered perception, we observed a significant association for Pe error amplitudes (r=-.518, p=.004) – those participants (n=6) reporting this side effect at the end of the experiment had less positive Pe amplitudes in error trials. No other correlation coefficients were significant (all p’s>.032).

38 Pfabigan, D.M.

None of the side effect categories correlated with the subjective ratings during the task (worry: all p’s>.69; comfortable: all p’s>.33). 15. Additional heart rate analysis We further explored whether a peripheral correlate of error commission (heart rate deceleration after erroneous responses, which is assumed to mark significant environmental changes (Danev and de Winter, 1971) was sensitive to the administration of buprenorphine. Analysis: Heart rate analysis was performed using algorithms provided by the FMRIB plug-in in EEGLAB (Niazy et al., 2005). First, R peaks of the QRS complex in the ECG were identified automatically. Afterwards, data were visually controlled for artefacts as well as for trials in which R peaks had been detected incorrectly. The distance between two consecutive R peaks was used to compute the respective inter-beat interval using linear interpolation (Niazy et al., 2005; Wessel et al., 2011). Data of one participant in the BUP group was not available for analysis because R peak detection could not be performed successfully. Subsequently, data were epoched in reference to the button press from -1000ms to 2000ms. Mean inter-beat values were extracted in 500ms intervals. The 500ms interval before the response served as baseline for the subsequent four time frames and was subtracted from each following time frame. These data were winsorised to account for potential outliers (Wilcox, 2012). Heart rate data were subjected to MLM with time as a subject-level variable (mean-centred effected- coding: TF1 [1-500ms]:-1.5, TF2 [501-1000]: -0.5, TF3 [1001-1500]: 0.5, TF4 [1501-2000]: 1.5), and correctness and drug-group as fixed factors, estimating a random intercept for participant and time. Results: Heart rate data were significantly moderated by correctness (b=-0.40, SE=0.06, t(190.68)=- 7.00, p<.01, semi-partial R2=0.20), time (b=-0.28, SE=0.07, t(33.09)=-4.27, p<.01, semi- partial R2=0.36), and their interaction (b=-0.30, SE=0.05, t(195.46)=-5.96, p<.01, semi- partial R2=0.15). The factor drug-group (p=.99) and interaction terms with it (all p’s>.67) were not significant. Post-hoc tests showed a significant heart rate deceleration for erroneous compared to correct responses for TF3 [1001-1500] (b=-0.52, SE=0.12, t(218.34)=-4.26, p<.01, semi-partial R2=0.08) and TF4 [1501-2000] (b=-0.89, SE=12, t(218.39)=-7.19, p<.01, semi-partial R2=0.19), while no significant differences were observed for the two earlier time windows (both p’s>.28).

XTable S7. Mean heart rate data BUP group Control group Heart rate M SD M SD TF1 correct -0.40 0.57 -0.49 0.40 error -0.43 0.51 -0.47 0.57 TF2 correct -1.15 1.02 -1.26 0.60 error -1.37 0.98 -1.49 1.16 TF3 correct -1.22 0.90 -1.23 0.59 error -2.13 1.20 -2.28 1.71 TF4 correct -0.45 0.64 -0.30 0.48

39 Pfabigan, D.M.

error -2.06 1.06 -2.13 2.30

Figure S4. Mean heart rate changes within 0–2000ms after correct (blue) and erroneous (green) responses averaged over all participants. Error bars denote 95% CI. TF1: 1-500ms; TF2: 501-1000ms; TF3: 1001-1500ms; TF4: 1501-2000ms

Changes in heart rate were only affected by error commission, but not by the administration of buprenorphine. Replicating results of previous studies including our own (Danev and de Winter, 1971; Fiehler et al., 2004; Hajcak et al., 2003; Pfabigan et al., 2016), errors led to a significant slowing in heart rate after 1000 ms following error commission. This deceleration is supposed to constitute a physiological correlate of the so-called orienting response (Sokolov, 1960), i.e., a reflex-like response to unexpected and possibly relevant environmental changes. It is associated with increased arousal (Sokolov, 1960). The absence of group differences here suggests comparable autonomous responding after the occurrence of an error. This contrasts with our findings on the neuronal level. However, heart rate deceleration is often not coupled with variation in error-related ERPs (Hajcak et al., 2003). Thus, it is plausible that the administration of a low dose of BUP exerts differential effects on central and autonomous nervous system processes.

40 Pfabigan, D.M.

16. Additional figures of the behavioural results

Figure S5. Mean reaction times (RT) of correct/incorrect trials (upper panel), congruent/incongruent trials (middle panel), and pre-error/post-error trials (lower panel) of the two groups is presented in box plots depicting the median and quartiles (whiskers demonstrating 1.5 times the interquartile range). The violin plots around illustrate kernel

41 Pfabigan, D.M. probability density (i.e., its width demonstrates the proportion of data located there); individual values are plotted within the violin plots.

17. Post-hoc analyses of stimulus-locked ERPs These post-hoc analyses were conducted to answer the question whether the administration of buprenorphine might rather exert influence on attentional processes than on cognitive control processes. This question was raised during the review process. We applied a similar strategy as in one of our previous studies (Pfabigan et al., 2015) and extracted amplitude variation of early visual ERPs sensitive to attentional manipulations in a first step – P1 and N1 components were extracted (Gomez Gonzalez et al., 1994; Hillyard and Anllo-Vento, 1998; Luck, 2005; Mangun, 1995; Rugg et al., 1987; Vogel and Luck, 2000). Furthermore, we assessed stimulus-locked P300 amplitude variation, which should be sensitive to stimulus congruency (Sebanz et al., 2006; Valle-Inclán, 1996; Zhou et al., 2004), with more positive P300 amplitudes for congruent than incongruent trials. In a last step, we also explored amplitude variation of the stimulus-locked N2 component which should reflect pre-response perceptual conflict induced by incongruent compared to congruent arrays (Folstein and Van Petten, 2008; Nieuwenhuis et al., 2003; Yeung et al., 2004). Based on the proposal by van Steenbergen and colleagues (2019), N2 amplitudes should be diminished in the buprenorphine compared to the control group.

We created stimulus-locked epochs starting 200 ms before the onset of the outer flankers and lasting for 1200 ms. The first 200 ms served as baseline interval. For all participants, these epochs were separated for congruent and incongruent flanker displays. Only those trials were selected that resulted in a correct response. We applied the same artefact-correction thresholds as for response-locked epochs. On average, 102 incongruent (SD=20.1) and 109 congruent (SD=17.6) trials were available after artefact correction; no group differences were observed here (Welch’s t-tests testing for group differences for congruent and incongruent trials, both p’s>.87). The subsequent time windows were chosen based on the literature and the current task set up, i.e., the central letter was displayed 100 ms after onset of the outer flankers. Thus, ERPs sensitive to the congruency information of the flanker arrays had to be extracted later than in Flanker task versions that present all five letters or arrows at once. We extracted mean amplitudes for the P1 component in the time window 110-140 ms after the onset of the outer flankers, for the N1 component in the time window 140-200 ms, in an occipital electrode cluster comprising electrodes Oz, O1, and O2 (Figure S1, electrodes R30, L28, L30). P300 amplitudes were extracted in the time window 400-600 ms after outer flanker onset in the Pz electrode cluster depicted in Figure S1. Lastly, N2 amplitudes were extracted 350-450 ms after outer flanker onset in the FCz cluster depicted in Figure S1. Data were checked for outliers and in case those were detected, a winsorisation procedure was applied as described in section 9 here in Supplementary Materials. A 2-level multilevel model was used accounting for congruency and drug-group nested within participants by estimating a random intercept and a random slope for each participant. We applied a restricted maximum likelihood estimations for the fixed effects, and the Satterthwaite method for estimating

42 Pfabigan, D.M. degrees of freedom (effect-coding: control group: 1; buprenorphine group: -1; congruent: -1; incongruent: 1). See Table S8 for means and SD per ERP component. For P1 amplitudes, no significant effects of drug-group, congruency, or their interaction were found (all p’s>.22). Also N1 amplitudes were not significantly affected by drug-group, congruency, or their interaction (all p’s>.29). For P300 amplitudes, we observed a significant main effect of congruency (b=-0.38, SE=0.15, t(33.0)=-2.54, p=.02, semi-partial R2=0.16), while drug group (p=.86) and the interaction (p=.25) were not significantly affected. P300 amplitudes were more positive for congruent than incongruent flanker arrays. For N2 amplitudes, we observed a significant main effect of congruency (b=-0.51, SE=0.12, t(33.0)=-4.12, p<.01, semi-partial R2=0.34), and a trend towards significance for the interaction (b=-0.22, SE=0.12, t(33.0)=, p=.08, semi-partial R2=0.09). The main effect of group was not significant (p=.271). N2 amplitudes were more negative for incongruent than congruent flanker arrays. Exploring the trend interaction result with simple slope analyses testing for group differences, no significant effects were observed for incongruent (p=.160) and congruent (p=.448) trials. Simple slope analyses testing for congruency effects showed a significant effect of congruency in control participants (b=-0.74, SE=0.19, t(33.0)=-4.40, p<.01, semi-partial R2=0.37), while the difference between congruent and incongruent trials was not significant in buprenorphine participants b=-0.29, SE=0.18, t(33.0)=-1.56, p=.13). Overall, these results suggest that the administration of buprenorphine did not influence neural measures of attentional processing in the current sample, both in terms of early stimulus-driven selective attention processes (P1, N1), as well as more elaborate attention processes of working memory update (P300). The only stimulus-locked ERP that showed a trend towards being sensitive to the current experimental manipulation was the N2 component related to stimulus conflict processing. Pointing in the same direction as the response-locked results, the administration of buprenorphine seemed to diminish participants’ sensitivity to distinguish between conflicting (i.e., incongruent) and non-conflicting (i.e., congruent) stimulus arrays. When plotting the figures for this post-hoc analysis, we noticed clear group differences between buprenorphine and control participants around 280 ms after the onset of the outer flankers at the frontal electrode cluster (Figure S6). We had no a priori hypothesis addressing congruency differences or attention effects in such a time window for the frontal cluster, in particular since ERP component overlap is an issue with the current flanker task version (i.e., 100 ms presentation of the outer flankers, then 35 ms full flanker array, then presentation of a black screen until button press). Thus, we refrain from speculating about the functional significance of this component and the group differences.

43 Pfabigan, D.M.

Figure S6. Grand average waveforms of incongruent and congruent amplitude courses at the frontal electrode cluster are depicted, plotted for the buprenorphine (BUP; black) and the control group (CON; grey). Time point zero indicates the onset of the outer flankers, after 100 ms the middle letter was blended into the array, at 135 ms the display switched from the full flanker array to a black screen.

44 Pfabigan, D.M.

Figure S7. The upper panel depicts grand average waveforms of incongruent and congruent amplitude courses at the occipital electrode cluster plotted for the buprenorphine (BUP; black) and the control group (CON; grey). The lower panel depicts grand average waveforms of incongruent and congruent amplitude courses at the parietal electrode cluster plotted again separately for the two groups. Time point zero indicates the onset of the outer flankers, after 100 ms the middle letter was blended into the array, at 135 ms the display switched from the full flanker array to a black screen.

Table S8. Means and SD of attention ERPs

BUP group Control group M SD M SD P1 congruent -0.89 2.36 -1.98 2.82 incongruent -0.93 2.08 -1.99 2.89

45 Pfabigan, D.M.

N1 congruent -2.47 2.61 -2.51 2.81 incongruent -2.29 2.88 -2.42 2.94 P300 congruent 9.92 4.83 10.01 4.07 incongruent 9.52 5.44 8.91 2.70 N2 congruent 6.66 4.57 5.62 3.52 incongruent 6.09 4.71 4.14 3.27

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