Elsevier

Schizophrenia Research

Volume 191, January 2018, Pages 43-50
Schizophrenia Research

Late deviance detection in rats is reduced, while early deviance detection is augmented by the NMDA receptor antagonist MK-801

https://doi.org/10.1016/j.schres.2017.03.042Get rights and content

Abstract

One of the most robust electrophysiological features of schizophrenia is reduced mismatch negativity, a component of the event related potential (ERP) induced by rare and unexpected stimuli in an otherwise regular pattern. Emerging evidence suggests that mismatch negativity (MMN) is not the only ERP index of deviance detection in the mammalian brain and that sensitivity to deviant sounds in a regular background can be observed at earlier latencies in both the human and rodent brain. Pharmacological studies in humans and rodents have previously found that MMN reductions similar to those seen in schizophrenia can be elicited by N-methyl-d-aspartate (NMDA) receptor antagonism, an observation in agreement with the hypothesised role of NMDA receptor hypofunction in schizophrenia pathogenesis. However, it is not known how NMDA receptor antagonism affects early deviance detection responses. Here, we show that NMDA antagonism impacts both early and late deviance detection responses. By recording EEG in awake, freely-moving rats in a drug-free condition and after varying doses of NMDA receptor antagonist MK-801, we found the hypothesised reduction of deviance detection for a late, negative potential (N55). However, the amplitude of an early component, P13, as well as deviance detection evident in the same component, were increased by NMDA receptor antagonism. These findings indicate that late deviance detection in rats is similar to human MMN, but the surprising effect of MK-801 in increasing ERP amplitudes as well as deviance detection at earlier latencies suggests that future studies in humans should examine ERPs over early latencies in schizophrenia and after NMDA antagonism.

Introduction

The mismatch negativity (MMN) is a component of the event-related potential (ERP) that occurs following exposure to unexpected stimuli, and is typically measured from electroencephalography (EEG) using an auditory oddball sequence (Näätänen, 1992). In the oddball sequence, a regular, frequent, and predictable pattern of auditory stimuli, the standards (STDs), is occasionally interrupted by a rare (e.g. 10%), unexpected auditory stimulus, the deviant (DEV) (Kujala et al., 2007). MMN is typically observed as a negative deflection in the DEV ERP relative to the STD at approximately 100–250 ms and the majority of human studies quantify MMN as the arithmetic difference between the DEV and STD responses (Picton et al., 2000). To control for the physical characteristics of the stimuli, a flip-flop oddball sequence is sometimes employed, where the roles of stimuli as standards or deviants are reversed in successive oddball sequences, allowing the response to a stimulus when it is a DEV to be compared to the same stimulus when it is a STD (Harms et al., 2014).

The amplitude of the MMN (quantified as DEV - STD) has been consistently (> 100 studies) found to be reduced in individuals with schizophrenia (Bodatsch et al., 2015, Erickson et al., 2016, Umbricht and Krljes, 2005). In addition, administration of N-methyl-d-aspartate (NMDA) receptor antagonists (such as PCP, ketamine) reduces MMN in healthy controls [for review, (Todd et al., 2013)]. These findings complement other studies demonstrating that NMDA receptor antagonists produce a wide range of schizophrenia-like symptoms in healthy subjects, including positive, negative and cognitive symptoms (Corlett et al., 2007, Domino and Luby, 2012, Krystal et al., 1994) and provide support for the glutamate hypothesis of schizophrenia, that NMDA receptor hypofunction is a major contributor to the pathophysiology of schizophrenia (Javitt et al., 2012).

The sensitivity of MMN to cognitive and psychosocial functioning impairments in schizophrenia, as well as its promise in predicting transition to schizophrenia, has led to it being proposed as a neurophysiological biomarker of schizophrenia (Light and Swerdlow, 2015). It is therefore increasingly important to gain a greater understanding of the neurobiological mechanisms underlying MMN. In addition, the development of pharmacological interventions that target MMN may prove useful for the treatment of the disorder. As a consequence, considerable recent research has focused on developing rodent models of schizophrenia-like MMN reductions. Several studies have demonstrated that the rat brain is capable of generating a MMN-like response (or mismatch response, MMR) [for review, (Harms et al., 2016)]. In addition, it has also been demonstrated that schizophrenia-like reductions in MMRs can be induced by NMDA receptor hypofunction (either genetic or pharmacological) (Featherstone et al., 2014, Sivarao et al., 2014), as well as in developmental models of schizophrenia (Cabungcal et al., 2014, Witten et al., 2014). As reviewed in Harms (2016), many of these studies have not yet utilized a systematic control to differentiate the effects of deviance detection from neural refractoriness or adaptation. Briefly, two separate mechanisms could contribute to an increase in the DEV response relative to the STD: i) Adaptation: The DEV is presented less often than the STD and therefore the response to the DEV is less affected by adaptation (the reduction of responsiveness of a neural population specifically to repeated stimuli) and ii) Deviance Detection: the DEV violates a predictive model established by repeated presentation of the STD and the response to the DEV contains a ‘prediction error’ response (Friston, 2005, Garrido et al., 2009, Wacongne et al., 2012). These two mechanisms can be differentiated through use of the many standards control sequence (Jacobsen and Schroger, 2001). Responses to physically identical control stimuli (CON), that are presented as infrequently as DEV stimuli (thus controlling for adaptation), but do not violate a regular sequence, can be used to calculate a measure of “true” deviance detection (DEV-CON) (Harms et al., 2016). It is proposed that the NMDA receptor, as one of the key molecules responsible for the development of synaptic plasticity, is required to form a predictive model of the auditory environment that leads to predictions of upcoming tones based on regularities in past inputs (Wacongne et al., 2012). Thus, antagonists of the NMDA receptor inhibit the formation of a predictive model and therefore reduce the resulting ‘prediction error’ signal that is reflected in MMN signal (Wacongne, 2016).

Recent human studies have demonstrated that deviance detection is not limited to MMN. When responses in the middle-latency range (MLR, positive and negative deflections peaking between 10 and 50 ms after stimulus onset) are recorded with an appropriate frequency response, sufficiently fast digitization and very high stimulus repetitions, it has been found that several MLR components exhibit increased response to the DEV compared to the STD. When compared to a many standards CON response, some MLRs were observed to reflect true deviance detection (Cornella et al., 2012, Escera et al., 2013, Grimm et al., 2011, Slabu et al., 2010). No animal or human studies of deviance detection or other MMN-like effects have focused on the effect of NMDA receptor manipulations on these early components.

The aim of the current study was to investigate the impact of pharmacological NMDA antagonism, known to reduce MMN in humans, on MMRs in rats, with a focus on adaptation-independent deviance detection, and on early components similar to the human MLRs.

Section snippets

Animals and surgery

All experiments were performed under strict adherence to the National Health and Medical Research Council's Australian code of practice for the care and use of animals for scientific purposes and were approved by the University of Newcastle's Animal Care and Ethics Committee (Approval number A-2009-108). Surgical procedures were performed under well-maintained anaesthesia and all efforts were made to reduce the number of animals used and alleviate pain and discomfort following surgery through

Presence of deviance detection in drug-free condition

Mean amplitude responses of the five distinct ERP components for DEV, CON and STD responses are shown in Table 1. Deviance detection in the drug-free condition was confirmed by the observation of a significant difference between DEV and CON responses. P13 responses approached significance for deviance detection (F(1,16) = 4.05, p = 0.061; Table 1). Significant deviance detection was observed for the later, negative components N55 (F(1,16) = 11.06, p = 0.004; Table 1) and N85 (F(1,16) = 18.89, p < 0.001;

Discussion

In this study, we report a thorough characterisation of the effects of pharmacological NMDA antagonism on deviance detection in rats over a range of latencies including late MMN-like responses. Previous investigations have examined the impact of NMDA antagonism on MMRs in rats but to our knowledge, this is the first study to characterise the responses to several doses and to report responses to the requisite stimuli that contribute to MMRs (DEV, CON, STD) in an effort to quantify

Conclusions

In the current study, we observed that MK-801 reduced deviance detection of a late negative component (N55). MMRs at this N55 component meet the rigorous standards for being considered ‘MMN-like’ in that deviance detection is observed. The N55 component is therefore a potential target for further investigation of the neurobiology of deviance detection. However, the deviance detection-increasing effects of MK-801 in early components of the ERP combined with increased amplitude of these early

Conflict of interest

The authors declare no conflicts of interest.

Contributors

LH, JT, US, DMH and PTM conceived of and designed the experiments. WRF designed and refined the recording set up used for the experiments. LH and CM undertook the experiments. LH and WRF analysed the data for the experiments and LH wrote the first draft of the manuscript. All authors contributed to and approved the final manuscript.

Funding

This project was funded by a National Health & Medical Research Council of Australia (NHMRC project grant 1026070) and The University of Newcastle's Faculty of Science and IT for equipment support. Salary and stipend support was provided by the Australian Postgraduate Award (CM), NHMRC project grant 1026070 (LH) and the University of Newcastle Priority Research Centre for Brain and Mental Health Research (WRF). US was supported by the Schizophrenia Research Institute utilizing infrastructure

Acknowledgements

We thank Tony Kemp, Gavin Cooper and Jason Nolan for technical assistance.

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