The auditory mismatch negativity (MMN) is an electrophysiological response to rule violations in auditory input streams (Näätänen et al., 2001; Näätänen et al., 2011). It is commonly defined as the difference between event-related potentials (ERPs) to predictable (‘standard’) and surprising (‘deviant’) auditory events, and has been interpreted as reflecting the update of a predictive (generative) model of the acoustic environment (Winkler, 2007; Garrido et al., 2009; Lieder et al., 2013a; Lieder et al., 2013b; Weber et al., 2020).

The auditory MMN is of major interest for translational research in psychiatry. First, there is strong evidence that MMN amplitudes are significantly reduced in patients with schizophrenia (for meta-analyses, see Umbricht and Krljes, 2005; Erickson et al., 2016; Avissar et al., 2018). Second, numerous studies in animals and humans have demonstrated convincingly that the MMN is sensitive to pharmacological alterations of NMDA receptor (NMDAR) function (Javitt et al., 1996; Umbricht et al., 2000; Heekeren et al., 2008; Schmidt et al., 2012; Rosburg and Kreitschmann-Andermahr, 2016) – which, in turn, plays a major role in pathophysiological theories of schizophrenia (Olney and Farber, 1995; Friston, 1998; Goff and Coyle, 2001; Stephan et al., 2006; Stephan et al., 2009; Corlett et al., 2011; Corlett et al., 2016; Javitt, 2012; Friston et al., 2016). The MMN has thus been suggested as a potential readout of NMDA receptor (NMDAR) hypofunction in schizophrenia and has been proposed as a promising translational biomarker (Light and Näätänen, 2013; Todd et al., 2013; Näätänen et al., 2015).

We have recently demonstrated that the dependency of the MMN on intact NMDAR signaling can be understood in terms of hierarchical inference about the world’s statistical structure (Weber et al., 2020): After blocking NMDARs with ketamine, the MMN still reflected updates about the regularity in the tone sequence (lower level prediction errors, PEs), but updates about the stability of this regularity (higher level PEs) were significantly reduced. Theoretical accounts (Behrens et al., 2007; Mathys et al., 2011) predict that the level of stability in the environment should impact on lower level belief updates by scaling the certainty with which beliefs are held (the precision-weight on the PE). Consistent with this, the MMN has previously also been found to be sensitive to the overall level of volatility within a block, such that MMN amplitudes were higher in blocks with more stable regularities (Todd et al., 2014; Dzafic et al., 2020).

Here, we investigate whether auditory mismatch responses and their dependence on environmental volatility are differentially sensitive to cholinergic versus dopaminergic challenges. Acetylcholine (ACh) and dopamine (DA) are two modulatory transmitters with a general capacity to modulate NMDAR function (Hallett et al., 2006; Lin et al., 2010; Zappettini et al., 2014; Zwart et al., 2018; for review, see Gu, 2002), and their relative contribution to NMDAR dysregulation has been suggested as a major cause of heterogeneity in clinical trajectories among patients with schizophrenia (‘dysconnection hypothesis’, Stephan et al., 2006; Stephan et al., 2009).

Understanding the substantial heterogeneity within patient populations under the current syndromatic diagnostic categories is one of the main challenges for psychiatry and an essential basis for individualized treatment predictions (Kapur et al., 2012; Stephan et al., 2017). As a consequence, biomarkers are sought that differentiate between alternative pathophysiological mechanisms where, ideally, these mechanisms relate to different available treatment options.

Critically, detecting alterations of cholinergic and dopaminergic neuromodulatory transmitter systems may indeed be relevant for treatment choice in schizophrenia: while standard antipsychotic treatment options in schizophrenia rely on antagonism at D2/D3 dopaminergic receptors, they show considerable variability in their binding capacity to other receptors (Nasrallah, 2008). Most notably, some of the most potent antipsychotics (olanzapine and clozapine) have strong affinity to cholinergic (specifically: muscarinic) receptors (Lavalaye et al., 2001), in contrast to almost all other second generation antipsychotics. Therefore, a readout of the functional status of muscarinic vs. dopaminergic systems in the individual could prove valuable for understanding the neurobiological basis of differential treatment responses in schizophrenia, and, subsequently, for guiding treatment (Stephan et al., 2009; Stephan et al., 2015).

However, whether such a readout of muscarinic vs. dopaminergic function could be obtained from MMN responses is not clear. While nicotinic stimulation has been demonstrated to enhance MMN amplitudes (Harkrider and Hedrick, 2005; Inami et al., 2005; Inami et al., 2007; Baldeweg et al., 2006; Dunbar et al., 2007; Martin et al., 2009; Fisher et al., 2012; Knott et al., 2012; Hamilton et al., 2018), the role of muscarinic cholinergic receptors for MMN is less well established. The few human studies investigating the effects of muscarinic antagonists scopolamine and biperiden on auditory mismatch processing were inconclusive and showed mixed results (Pekkonen et al., 2001; Pekkonen et al., 2005; Klinkenberg et al., 2013; Caldenhove et al., 2017). Similarly, while several pharmacological studies of DA failed to show significant effects on MMN (Kähkönen et al., 2002; Leung et al., 2007; Leung et al., 2010; Korostenskaja et al., 2008), other studies reported significant alterations of MMN by antipsychotic drug treatment, hinting at a possible effect of DA (Kähkönen et al., 2001; Zhou et al., 2013). However, the latter interpretation is vague, given that the antipsychotic drugs studied affect numerous types of receptors.

In summary, there is inconclusive evidence concerning the sensitivity of the auditory MMN to dopaminergic and muscarinic alterations. This could be due to small sample sizes, unspecific drugs (such as antipsychotics), and/or individual differences in pharmacokinetics and thus variability in actual drug plasma levels across participants.

Here, we report results from two double-blind, between-subject, placebo-controlled studies that address these problems and test whether auditory mismatch responses and their dependence on volatility are differentially sensitive to cholinergic and dopaminergic alterations. In study 1 (N = 81), we tested the effects of biperiden, a selective muscarinic M1 receptor antagonist, on mismatch related ERPs, and compare them to the effects of amisulpride, a selective dopaminergic D2/3 receptor antagonist. In study 2, we employed exactly the same study design, paradigm, and analysis strategy in a separate sample (N = 81), to test the impact of elevated cholinergic vs. dopaminergic transmission on mismatch amplitudes, contrasting the acetylcholinesterase inhibitor galantamine to the dopamine precursor levodopa. In both studies, we used estimates of the actual drug plasma levels at the time participants performed the experimental task in order to account for individual differences in pharmacokinetics.

Importantly, we used a new variant of an auditory oddball paradigm with explicitly varying levels of stability over time. We were interested in whether cholinergic manipulations would interact with the MMN’s sensitivity to environmental volatility. This was motivated by theoretical accounts (Mathys et al., 2011) and experimental findings that volatility affects precision-weighting of prediction error responses, possibly via interactions of ACh and NMDA-receptor-dependent mechanisms (Iglesias et al., 2013; Weber et al., 2020). While these latter studies employed trial-by-trial computational models, our paradigm was designed to contain distinct phases of volatility. This allowed us to capture the interaction between environmental volatility and mismatch processing using the conventional approach of trial averaging and comparing mismatch responses across different phases of volatility, which maximizes sensitivity for detecting volatility effects. Here, we focus on the results of this conventional analysis, but also present the (complementary) model-based perspective on our data in Appendix 1.

Based on previous literature, one would expect mismatch responses in our paradigm to be sensitive to (1) volatility, with larger mismatch amplitudes during more stable phases (Todd et al., 2014; Dzafic et al., 2020; Weber et al., 2020), and (2) cholinergic manipulations, with galantamine increasing and biperiden reducing mismatch amplitudes (Moran et al., 2013; Schöbi et al., 2021). Furthermore, we expected (3) a differential effect of cholinergic (muscarinic) and dopaminergic receptor status on mismatch responses, as postulated by initial work on MMN-based computational assays (Stephan et al., 2006). Our results suggest that muscarinic receptors play a critical role for the generation of mismatch responses and their dependence on environmental volatility, whereas no such evidence was found for dopamine receptors.

We conducted two separate studies to test the effects of antagonizing dopamine and muscarinic acetylcholine receptors (study 1: amisulpride and biperiden) and of elevating dopaminergic and cholinergic signaling (study 2: levodopa and galantamine) on auditory mismatch responses.

In each of the two studies, 81 healthy male volunteers were randomly assigned to receive either placebo, or one of two study drugs, with both participants and researchers blind to drug assignment. After data exclusion, N = 71 datasets entered the group analyses for study 1 (placebo: N = 25, amisulpride: N = 24, biperiden: N = 22), and N = 78 for study 2 (N = 26 per group).

All steps of our analysis pipeline, including data exclusion criteria and statistical contrasts of interest, were specified in a time-stamped analysis plan prior to the unblinding of the researcher conducting the analysis (see Materials and Methods, section Analysis plan, data and code availability).

MMN paradigm and distraction task

We used a new variant of the auditory oddball paradigm, in which we explicitly varied the degree of volatility in the auditory stream over time. In a classical oddball paradigm, one stimulus is less likely to occur and thus considered a surprising, or ‘deviant’, stimulus, whereas the other stimulus is considered the ‘standard’ event. Our sequence was generated such that both tones could be perceived as standard (predictable) or deviant (surprising), depending on the current context. Volatile phases were defined by more frequent context switches (every 25–60 trials). Figure 1A displays the probability structure underlying the tone sequence and the division into stable and volatile phases.

Figure 1

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Following previous studies (Garrido et al., 2008), we only considered those tones as deviants which followed at least 5 repetitions of the other tone (resulting in N_deviants = 119). Equivalently, we defined standards as the 6th repetition of a tone (N_standards = 106) in order to keep trial numbers comparable across conditions. We chose this trial definition due to its specificity; however, we also considered alternative trial definitions (specified as part of our analysis plan) which allow for higher trial numbers per condition and tested the robustness of our results to this choice (we report the results of these additional analyses as figure supplements in the relevant places).

During EEG recording, participants passively listened to the tone sequence while engaging in a visual distraction task, following the suggestion that MMN assessment is optimal when the participant’s attention is directed away from the auditory domain (Näätänen, 2000). Their task was to indicate via button press whenever they detected a centrally presented visual target (Figure 1B; number of targets in study 1: 36; study 2: 90). Based on the participants’ responses, we calculated mean reaction times and hit rates (defined as the proportion of correct responses relative to the total number of visual targets). Due to technical issues during measurement, behavioral data from three participants in study 1 (N = 1 amisulpride group, N = 2 biperiden group) were missing.

Participants reacted to visual targets on average after 509.8 ms (SD = 72.6; study 1) and 460.4 ms (SD = 54.2; study 2) and responded correctly to 94.8% (SD = 7.0; study 1) and 95.6% (SD = 5.3; study 2) of the presented targets. There were no significant differences between drug groups in their performance in study 1, as assessed with a one-way ANOVA for reaction times (F = 1.32, p = 0.27) and a Kruskal Wallis test for hit rates (χ² = 2.92, p = 0.23, Figure 1C). In study 2, reaction times again did not differ significantly between drug groups (ANOVA F = 0.55, p = 0.58), but there was a significant effect of drug group on hit rates (Kruskal-Wallis χ² = 8.36, p = 0.01, Figure 1D). Post-hoc pairwise comparisons indicated that hit rates in the galantamine group were significantly higher than in the levodopa group (p = 0.019; the difference to the placebo group failed to reach significance: p = 0.06). This result also held when excluding the participant with a hit rate below 75% (now placebo N = 25; χ² = 8.36, p = 0.018; galantamine > levodopa p = 0.017; galantamine > placebo p = 0.104).

We cannot exclude the possibility that the participants with missing behavioral data, as well as one participant per study with very low performance level on the distraction task (hit rate << 75%, placebo groups, see Figure 1C and D), were paying attention to the auditory input instead of focusing on the visual task. We therefore additionally provide the results of our group ERP analysis after excluding these data sets (Supplementary file 1).

We analyzed trial-wise EEG responses in our paradigm using a factorial design with the within-subject factors ‘mismatch’ (standards versus deviants) and ‘stability’ (stable versus volatile; both factors implemented at the first level) and the between-subject factor ‘drug’ (study 1: placebo vs. amisulpride vs. biperiden; study 2: placebo vs. levodopa vs. galantamine). To account for interindividual differences in pharmacokinetics, drug plasma concentration levels per participant (obtained via blood samples, see Materials and Methods) entered the group-level GLM as a covariate. In the following, we report the group-level effects, separately for both studies, for the main effect of mismatch, its interaction with drug group, the interaction between mismatch and stability, and the three-way interaction mismatch × stability × drug. In Appendix 2, we additionally report the main effect of stability and its interaction with drug.

Biperiden delays and topographically shifts the MMN

In study 1, mismatch effects were different between drug groups: biperiden delayed and topographically shifted mismatch signals compared to amisulpride and placebo (see Figure 2A for selected sensors, and Figure 2B for selected time points). When considering the whole time × sensor space and correcting for multiple comparisons using Gaussian random field (GRF) theory, this difference was significant at pre-frontal sensors for the comparison between the amisulpride and the biperiden group: between 160 ms and 172 ms after tone onset, the difference between standard and deviant ERPs was significantly smaller in the biperiden group compared to the amisulpride group, peaking at 164 ms (t = 4.45, p = 0.012, Figure 2C, Table 1).

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Table 1

Study 1: Mismatch	cluster	$x\left[\mathrm{m}\mathrm{m}\right]$	$y\left[\mathrm{m}\mathrm{m}\right]$	$z\left[\mathrm{m}\mathrm{s}\right]$	$t_{66}$	$Z_{\equiv }$	$p_{FWE}$	$k_E$	$t{w}_{sig}$[ms]
A standards > deviants	1	–13	2	172	16.59	Inf	0.000	8,217	100–232
	2	0	13	400	8.24	6.81	0.000	1,037	364–400
	3	–13	50	276	6.66	5.81	0.000	284	240–300
	4	-4	–95	304	5.38	4.88	0.002	135	284–328
	5	–60	–57	268	4.76	4.40	0.015	166	240–280
		–60	–36	260	4.73	4.38	0.016
		–60	–46	264	4.73	4.37	0.016
B deviants > standards	1	–47	–68	176	14.42	Inf	0.000	6,293	100–328
		64	–62	200	13.55	Inf	0.000
		42	–78	164	11.83	Inf	0.000
	2	17	72	168	13.93	Inf	0.000	1,592	100–236
	3	34	–46	364	6.10	5.41	0.000	336	352–400
		47	–52	400	5.27	4.80	0.003
	4	–51	–30	400	5.79	5.19	0.000	286	376–400
	5	4	72	400	4.56	4.24	0.027	5	400–400
	6	26	67	400	4.46	4.15	0.036	1	400–400
C AMI > BIP	1	8	67	164	4.83	4.45	0.012	31	160–172

To understand whether this mismatch × drug interaction was driven more by standard or deviant tones or both, we compared responses to standards and deviants separately within the significant cluster (cluster 1 in Table 1C, k = 31). Both effects were visible: responses to standards were more positive under biperiden compared to amisulpride (Cohen’s d = 0.83 at the peak voxel, t = 2.79) and responses to deviant tones were less positive under biperiden compared to amisulpride (peak d = 0.92, t = 3.15), together leading to a reduced mismatch (MMN) under biperiden.

No additional clusters showed significant mismatch × drug interactions when constraining the search volume to the significant average mismatch effect using the functionally defined mask. However, because mismatch effects in our large sample were significant in large portions of the time × sensor space (see Figure 2—figure supplement 1), this mask was rather unspecific. We therefore decided to deviate from our a priori analysis plan and constrain our search volume further by considering only those parts of the time × sensor space which both showed significant effects of mismatch in our sample and corresponded to the classical time windows and sensor locations for the mismatch negativity. In particular, we used the large cluster of frontal, fronto-central and central sensors described above which showed significant mismatch negativity between 100ms and 232 ms (peak t = 16.59) as a mask to constrain the search volume and subsequently constrain the multiple comparison correction to this volume using SPM’s small volume correction (SVC). When focusing on this subspace, an additional cluster showed a significant effect of drug on mismatch: mismatch signals were stronger in the biperiden group compared to the placebo group at right central and centro-parietal sensors with peak difference at 200 ms (t = 3.72, p = 0.048 after SVC). In this cluster (k = 16), only the effect on the deviant responses showed a large effect size (deviant responses were more negative under biperiden with a peak d = 1.13, t = 3.88), while the effect on standard responses was negligible (peak d = 0.25, t = 0.86), suggesting that this later interaction effect is mainly driven by a modulation of responses to deviant tones.

Together, these differences are indicative of both a delay and a shift in topography of mismatch signals in the biperiden group compared to the other two groups, leading to weaker mismatch early on, particularly in pre-frontal and frontal channels, but stronger mismatch later on, particularly in right centro-parietal channels (see Figure 2B for a visualization).

Figure 2—figure supplement 1 displays the main effect of mismatch (averaging across drug groups) in our paradigm. This contrast served to confirm that our paradigm elicited a classical mismatch negativity comparable with previous reports: in a large cluster of frontal, fronto-central, and central sensors, ERPs to standard tones were significantly more positive than ERPs to deviant tones from 100 to 232 ms after tone onset, with a peak difference at 172 ms (t = 16.59, p < 0.001). The reverse was true at pre-frontal (100 to 236 ms, peak at 168 ms, t = 13.93, p < 0.001) and temporo-parietal sensors (100 to 328 ms, peak at 176 ms, t = 14.42, p < 0.001). We found eight additional clusters of significant differences between standard and deviant ERPs at later time points within peristimulus time, which are listed in Table 1 and partly displayed in Figure 2—figure supplement 1.

Mismatch responses are stronger during stability

Our paradigm allowed us to test whether auditory mismatch signals would depend on the current level of stability in the sensory input. In study 1, we found such an interaction effect (mismatch × stability) in 3 clusters. Between 180 and 220 ms after tone onset, mismatch was significantly stronger in stable as compared to volatile phases, with a peak difference at 204 ms (t = 5.13, p = 0.001) at central and centro-parietal sensors. Right parietal and left temporo-parietal sensors, which generally show the mismatch effect with the opposite sign compared to fronto-central channels, also showed stronger (negative) mismatch for stable phases than for volatile phases (right parietal cluster: 188–236 ms, peak at 200 ms, t = 5.07, p = 0.001; left temporo-parietal cluster: 200–220 ms, peak at 208 ms, t = 5.33, p = 0.003; see Table 4 and Figure 3).

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Table 4

Study 1: mismatch × stability	cluster	$x\left[\mathrm{m}\mathrm{m}\right]$	$y\left[\mathrm{m}\mathrm{m}\right]$	$z\left[\mathrm{m}\mathrm{s}\right]$	$t_{66}$	$Z_{\equiv }$	$p_{FWE}$	$k_E$	$t{w}_{sig}$[ms]
A stable MMN > volatile MMN	1	17	–19	204	5.73	5.14	0.001	591	180–220
		–17	–25	196	5.68	5.10	0.001
B volatile MMN > stable MMN	1	42	–78	200	5.63	5.07	0.001	119	188–236
		55	–68	200	5.17	4.72	0.005
		64	–62	200	5.04	4.62	0.007
	2	–60	–57	208	5.33	4.85	0.003	29	200–220

Interaction effects in central channels reflected the following pattern: responses to standard tones were more positive and responses to deviant tones more negative during stable phases than during volatile phases (Figure 3D). The opposite was true for interaction effects at temporo-parietal clusters. This indicates that mismatch negativity, as defined by the difference between standards and deviants, was stronger during more stable periods.

In study 2, ERPs showed no significant interaction effects between the factors mismatch and stability. In other words, mismatch responses did not differ between stable and volatile periods of the experiment. Again, we tested the robustness of these findings with respect to our analysis choices. We found that either applying a more aggressive correction of slow drifts or adopting a trial definition which retained more trials per condition revealed significant interaction effects equivalent to those seen in study 1 under the original pipeline (Figure 3—figure supplement 1).

Above, we presented results from two pharmacological EEG studies which were designed to test the sensitivity of a new auditory oddball paradigm to cholinergic and dopaminergic modulations of synaptic plasticity. In study 1, we found that biperiden, a selective muscarinic (cholinergic) M1 receptor antagonist, delays and topographically shifts mismatch responses in this paradigm, while we did not observe this effect when inhibiting dopaminergic D2/3 receptor transmission by administration of amisulpride. Neither elevated cholinergic nor dopaminergic transmission, as induced in study 2 by galantamine and levodopa, respectively, resulted in observable changes to deviance processing in our task.

Our paradigm allowed us to examine processing of auditory deviants in two different contexts: during stable phases of the experiment, one tone reliably served as the ‘deviant’ (i.e., the unlikely) event, and the other as the ‘standard’. During volatile phases, the roles of standard and deviant switched more rapidly, requiring faster updating of the internal model of the acoustic environment. We found that antagonizing muscarinic cholinergic receptors with biperiden affected deviance processing particularly during stable phases of our task along with a significant interaction between deviance and stability.

In the following, we will first discuss the pharmacological effects on mismatch responses in the light of previous literature, then examine the influence of environmental volatility on mismatch processing and how this interacted with biperiden. Finally, we will discuss the clinical implications of our findings for treatment selection in schizophrenia.

All raw data (EEG data, behavior) used for this manuscript are available at https://research-collection.ethz.ch/handle/20.500.11850/477685, adhering to the FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles. The analysis code that reproduces the results presented here is publicly available on the GIT repository of ETH Zurich at https://gitlab.ethz.ch/tnu/code/weber-muscarinic-mmn-erp-2021.

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Author details

1. Lilian Aline Weber

   Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing - original draft, Writing – review and editing

   For correspondence

   weber@biomed.ee.ethz.ch

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9727-9623

2. Sara Tomiello

   Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland

   Contribution

   Data curation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

3. Dario Schöbi

   Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Katharina V Wellstein

   Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Daniel Mueller

   Institute for Clinical Chemistry, University Hospital Zurich, Zurich, Switzerland

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

6. Sandra Iglesias

   Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland

   Contribution

   Conceptualization, Project administration, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1778-7239

7. Klaas Enno Stephan

   1. Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland
   2. Max Planck Institute for Metabolism Research, Cologne, Germany

   Contribution

   Conceptualization, Funding acquisition, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8594-9092

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