Differential effects of NMDAR antagonists on working memory and gamma oscillations, and the mediating role of the GluN2D subunit

Differential effects of NMDAR antagonists on working memory and gamma oscillations, and the mediating role of the GluN2D subunit | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Differential effects of NMDAR antagonists on working memory and gamma oscillations, and the mediating role of the GluN2D subunit Rachel Hill, Chitra Vinnakota, Matthew Hudson, Kazutaka Ikeda, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5412811/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Working memory relies on synchronised network oscillations involving complex interplay between pyramidal cells and GABAergic interneurons. NMDA receptor (NMDAR) antagonists influence both network oscillations and working memory, but the relationship between these two consequences has not been elucidated. This study aimed to determine the effect of NMDAR antagonists on network oscillations during a working memory task in mice, and the contribution of the GluN2D receptor subunit. After training wildtype (WT) and GluN2D-knockout (KO) mice on the Trial-Unique-Non-match to Location (TUNL) touchscreen task of working memory, recording electrodes were implanted into the prefrontal cortex (PFC) and hippocampus. Mice were challenged with either (S)-ketamine (30mg/kg), (R)-ketamine (30mg/kg), phencyclidine (PCP, 1mg/kg), MK-801 (0.3mg/kg) or saline prior to TUNL testing while simultaneous local field potential recordings were acquired. PCP disrupted working memory accuracy in WT (p=0.001) but not GluN2D-KO mice (p=0.79). MK-801 (p<0.0001), (S)-ketamine (p<0.0001) and (R)-ketamine (p=0.007) disrupted working memory accuracy in both genotypes. PCP increased baseline gamma (30-80Hz) power in the hippocampus in WT (p=0.0015) but not GluN2D-KO mice (p=0.92). All drugs increased baseline gamma power in the PFC in both genotypes (p<0.05). Low gamma was induced during the maintenance phase of the TUNL task and increased when mice correctly completed the task (p=0.024). MK-801 disrupted task-induced low gamma in both genotypes (p=0.04). In summary, PCP action involves the GluN2D subunit of the NMDA receptor in the hippocampus to alter baseline gamma power and working memory. Task-induced low gamma activity during maintenance aligns with task performance, and is disrupted specifically by MK-801. Biological sciences/Neuroscience Health sciences/Diseases/Psychiatric disorders/Schizophrenia Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Schizophrenia affects 0.29–0.7% 1, 2 of the world’s population but causes significant social and economic burden with disability-adjusted life years estimated at 15.1 million 2 . Approximately 80% of individuals with schizophrenia are unemployed and it is one of the top 20 causes of years lived with disability globally 3 . The economic impact of schizophrenia is estimated between 0.30–0.60% of GDP in high income countries incorporating both direct medical treatment costs as well as indirect economic loss related to criminal justice/homelessness, loss of tax revenue, and productivity losses for both people with schizophrenia and their caregivers 4 , 5 . While schizophrenia is characterised by positive symptoms that “add” to one’s psyche, including delusions and hallucinations, as well as negative symptoms that “take away” from one’s psyche, such as social withdrawal, anhedonia and disordered thinking, cognitive symptoms are the best predictor of functional outcomes such as employment, independent living 6 – outcomes that carry significant financial burden. Cognitive symptoms include impairments in working memory, verbal learning, visual learning, attention, reasoning and problem solving and social cognition 7 . However, working memory (WM) in particular has been specifically related to long-term community functioning and as such has been identified as a core feature of schizophrenia 8 , 9 . Importantly, WM deficits are not treated by current antipsychotic medications, therefore there is an urgent need to better understand the mechanisms causing WM impairments in schizophrenia in order to develop evidence-based treatments. Neural oscillations play a crucial role in facilitating information processing and communication within and across brain regions 10 , 11 . Gamma oscillations (30-100Hz), in particular, are implicated in higher-order cognitive and sensory processing 12 , 13 and are evoked when humans perform tasks that require WM, attention, emotional processing and perception 14 – 18 . In people with schizophrenia increased baseline (or ongoing) gamma power but reduced cognitive task-induced gamma power, compared with healthy humans has been reported 19 – 23 . These aberrant changes in both resting-state and induced gamma oscillations have been linked to cognitive symptoms in people with schizophrenia 20 , 24 . For example, one study reported a significant negative correlation between resting-state gamma power and performance of a verbal learning task in people with schizophrenia 20 , while another reported significantly reduced gamma oscillations during a WM task in people with schizophrenia compared with controls 24 . Understanding neural oscillation dysfunction in schizophrenia may provide clues as to how to better treat WM impairment. NMDAR antagonists are well known to recapitulate the full spectrum of behavioural symptoms relevant to schizophrenia, and have therefore been used as tools to model schizophrenia symptoms in rodents 25 . Interestingly, NMDAR antagonists evoke similar gamma oscillatory changes to those found in schizophrenia. NMDAR antagonists like ketamine, PCP and MK-801 have repeatedly been shown to increase ongoing gamma power in the cortex and hippocampus and decrease stimulus-evoked gamma power in humans and rodent models 26 – 33 . The oscillatory and behavioural effects of these drugs promote the acute NMDAR antagonist model as one which can be used to explore the interrelationships between behaviour and electrophysiology relating to schizophrenia 34 . Although the precise biophysical and cellular mechanisms underlying neuronal oscillatory activity are not clear, there is extensive evidence implicating fast-spiking PV interneurons in the generation of neural oscillations within the gamma frequency range 35 – 37 . PV interneurons receive NMDAR-mediated excitatory input from pyramidal cells and in turn modulate neural network activity via synchronous and co-ordinated GABAergic inhibition of local excitatory neurons, resulting in gamma oscillations 10 , 37 , 38 . One leading theory for the aberrant gamma oscillatory changes in schizophrenia is the hypofunction of NMDARs on PV interneurons 39 – 41 . As GluN2D-containing NMDARs are especially enriched in PV interneurons 42 , 43 , and there is a reported reduction in the ketamine-induced increase in baseline gamma power in GluN2D-knockout (KO) mice compared with WT mice 44 , the objective of this study was to explore NMDAR antagonist-induced changes in neuronal oscillations in GluN2D-KO compared with WT mice during the performance of a WM task. There are several different methods to assess cognition in animal models. However, many of these methods bear little resemblance to how cognition is assessed in humans, which may contribute to the current lack of effective therapies for cognitive symptoms in schizophrenia. The Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) initiative was established in response to this need for therapies that improve functional outcomes in patients 45 . One of the main goals of CNTRICS was the development of tasks with a high degree of cognitive construct validity which could be used both in humans and in animal models. The rodent operant touchscreen system was thus created with the aim of closely resembling assessments of cognition used clinically to improve translatability and maximise efficiency in identifying appropriate therapeutic targets and treatments for schizophrenia 46 . The touchscreen system is translational, automated, non-aversive, low-stress, able to assess multiple cognitive domains within the same testing environment and can detect both impairments and enhancements in function 46 , 47 . This study utilises the trial-unique non-match to location (TUNL) touchscreen-based task to assess the influence of NMDAR antagonists on working memory in wild type (WT) and GluN2D-KO mice. In addition, for the first time, we combine touchscreen testing with simultaneous measurement of electrophysiological signals during task performance with the goal of identifying neural oscillation patterns which underpin drug and genotype effects on WM. METHODS Animals and housing GluN2D-KO mice were obtained from the Tokyo Metropolitan Institute of Medical Science and transported to the Monash Animal Research Platform, Monash Medical Centre (Clayton, Victoria, Australia) where a breeding colony was established. GluN2D heterozygous mice were bred to obtain WT, heterozygous and homozygous GluN2D-KO male and female littermates. At 6–7 weeks of age, mice were transferred from the breeding facility to the laboratories in the Department of Neuroscience, School of Translational Medicine, Monash University (Prahran, Victoria, Australia) where all husbandry, housing, and behavioral testing was undertaken. All mice (n = 42) were housed in groups of 2–5 in individually ventilated cages (Techniplast, NSW, Australia) with a reversed 12-h dark-light cycle (lights off at 9:30 am) allowing experiments to be conducted during the active phase of the mouse circadian cycle. Cages were monitored daily and changed weekly. After allowing mice to acclimatize to reverse light conditions (2 weeks), a food-restricted diet was gradually introduced (with water available ad libitum ), until mice reached 85–90% of their initial free-feeding weight, which was maintained throughout the testing period. All procedures were approved by the Monash University Animal Ethics Committee (Project #: E/1837/2018/M). Trial Unique Non-Matching to Location (TUNL) task of working memory The TUNL task was performed in the automated touchscreen operant chambers (Campden Instruments Ltd., UK) for mice, and Whisker and Abet II software (Campden Instruments Ltd., UK) were used to control the system and for data collection as previously described 48 , 49 . The TUNL task was conducted as previously described 47 – 50 . Briefly, mice habituated to the chambers were sequentially trained to: recognize visual stimuli and associate them with food delivery; nose-poke stimuli to trigger a reward; initiate the next trial by breaking an infrared (IR) beam near the reward collection tray; and lastly, to avoid touches to non-illuminated windows. Mice were advanced to TUNL training upon completing 48 trials within 30 mins at an accuracy above 80% over two consecutive days (see supplementary Table 1 for TUNL training protocol). Each TUNL session consisted of a maximum of 48 trials, each comprising two phases: during the sample phase, an initiation triggered by an IR beam break near the reward collection tray would result in the illumination of one window out of five possible locations. After the mouse nose-poked this stimulus, the stimulus disappears and a varying delay period begins. A second initiation triggers the start of the subsequent (choice) phase where a new location appears alongside the sample location requiring the mice to recall the sample location and choose the new location for a food reward. If the mouse chooses correctly, the delivery of the food reward is followed by an inter-trial interval (ITI) before the next trial. Whereas, a nose-poke to the incorrect location leads to a 5-s timeout and the initiation of a correction trial, where the same stimuli are presented repeatedly until a correct response is achieved. One session was conducted per mouse per day, occurring six days a week except for the pharmacological studies during which there was a minimum gap of 48 h between consecutive treatments. Full training details are outlined in Supplementary Table 1. During probe sessions mice were tested using S1c trials with a delay of 1 s between the sample and choice phase as our previous findings indicated that mice tend to use working memory rather than other strategies, like side bias, when tested using this configuration 48 . Drug challenge Mice were injected with either vehicle (0.9% saline) or S-ketamine (S-ket) (30 mg/kg), R-ketamine (R-ket) (30 mg/kg), PCP (1 mg/kg) or MK-801 (0.3 mg/kg) as previously described 51 . All mice randomly received each dose of all treatments with at least 48 h in between treatments. Each compound was delivered via a single intraperitoneal (i.p.) injection at 10 µl/g. Drugs were administered 10 mins before the testing session began for S-ket, R-ket and PCP and 30 mins before the testing session for MK-801. The primary outcome measure was task accuracy. During the drug challenge, the experimenter was blinded to the genotype of the mice but not the drug type or dose. Electrode implantation surgery Following the completion of behavioral experiments, mice underwent surgery to implant recording electrodes in the medial prefrontal cortex (mPFC) and dorsal hippocampus (dHPC) as previously described 52 , 53 . Mice were anaesthetized with 5% isoflurane, and 125 µm stainless steel recording electrodes (Cat # E363/3/SPC, 20 mm, PlasticsOne, Bioscientific, NSW, Australia) were implanted into the mPFC (AP: +1.9, ML: -0.4, DV: -1.7) and dHPC (AP: -1.8, ML: 1.3, DV: -1.3). A ground/reference electrode (Cat # E363/120/2.4, PlasticsOne, Bioscientific, NSW, Australia) was screwed into the cerebellum and two anchor screws were inserted on either side of the frontal plate. Electrodes were then connected to a multi-channel electrode pedestal (Cat # M52-5002545, Element 14, Australia) and secured to the skull using super glue and dental cement. Mice were administered buprenorphine for pain relief at the end of the surgery and allowed to recover for 7 days with food and water available ad libitum . Electrophysiological procedures Once TUNL performance was re-established post-surgery with the head-stage cable attached, mice were exposed to vehicle (0.9% saline), S-ket (30 mg/kg), R-ket (30 mg/kg), PCP (1 mg/kg) or MK-801 (0.3 mg/kg) via a single i.p. injection at 10 µl/g and were tested in TUNL probe sessions where combined electrophysiological and TUNL behavioural data were simultaneously recorded. Whisker and Abet II software (Campden Instruments Ltd., UK) were used to collect behavioral data whilst the electrophysiological data was and acquired using Multi Channel Systems software (Harvard Biosciences Inc., USA). A modified ABET TUNL schedule was used which allowed behavioural data to be synchronised with electrophysiological data. To record continuous LFPs during the test sessions, mice were connected via the head-mounted electrode pedestal to a custom-designed electrophysiology cable within the touchscreen chamber which was responsible for signal conditioning, multiplexing and digitising the analogue electrophysiological signal and transmitting the data to the acquisition hub where it was synchronised with behavioural data. The head stage cable was attached to a dragonfly commutator placed atop the touchscreen enclosure which prevented twisting of the cable when the mouse was performing the touchscreen task. Recordings were performed using Multi-Channel Systems Experimenter software (version # 2.8.2.18079, Harvard Biosciences Inc., USA), and sampled at 2000Hz. Electrophysiological analysis The time-stamped electrophysiology data was imported from Neuroexplorer (Plexon, USA) and analysed using custom-designed MATLAB (MathWorks, USA) scripts. Electrophysiology analysis consisted of two measurements: baseline activity and task-induced activity. Data was extracted from the relevant periods (see below for description). All epochs were first visually inspected for artefacts, with any epochs containing appreciable artefact manually rejected from subsequent analysis. While analysing the data, we found a small number of trials with a very long response time (maximal response time: 551.4 s). These longer response times were considered to be significantly beyond the WM capacity of mice. Upon plotting the response time of all trials (Supplementary Fig. 1), we found that 75% of trials were completed within 7s and thus, any trial that took greater than 7 s was excluded from further analysis. Measurement of baseline activity First, we examined the effect of the NMDAR antagonists on baseline oscillatory power in WT and GluN2D-KO mice. For this, we extracted data from a 5s window within the 12s intertrial interval (ITI) preceding each trial. All epochs were subject to spectral analysis using the multitaper method to compute the power spectral density (PSD) between 1 and 200Hz. This was achieved using the mtspectrumc function from the MATLAB Chronux plugin. The PSD estimate was then averaged across all baseline periods within each TUNL session to compute an average PSD. Ongoing power in the theta (5–10 Hz), beta (20–30 Hz), low gamma (30–40 Hz) and gamma (30–80 Hz) and HFO (100–200 Hz) frequency band was subsequently calculated by taking the integral of all values within the appropriate frequency interval. To generate power-spectral density plots, data was log transformed for graphical representation. Measurement of task-induced activity We next measured the effects of NMDAR antagonists on TUNL task-related oscillatory activity in WT and GluN2D-KO mice. Continuous LFP data was segmented into epochs from 0-2000 ms prior to selecting the sample stimulus (i.e.: the encoding phase), 0-2000 ms immediately following selection of the sample stimulus (i.e.: maintenance phase) and 0-2000 ms prior to selection of the choice stimulus (i.e.: retrieval phase) - see Fig. 4 A. Epochs were also categorised according to whether the mouse made a correct or incorrect choice in that specific trial. Then a time-frequency analysis was performed whereby the data was subjected to morlet wavelet decomposition using the EEGLab newtimef function in MATLAB. This calculated event related spectral perturbations (ERSPs) at 180 linearly spaced frequencies from 5–50 Hz with wavelet cycles increasing from 3 to 10. ERSPs indicated a task-evoked substantial increased power within the low gamma (30-40Hz) frequency band specifically during the maintenance phase of the task, therefore power at this frequency was extracted and represented as the change in power relative to the baseline period described above. Theta and gamma activity were also extracted and analysed through each phase of the task but power did not differ between correct and incorrect trials, therefore no further drug or genotype effect analysis was applied Data analysis All statistical analyses and graphical representations were generated using GraphPad Prism (version 8.3.1, GraphPad Software, San Diego). A two-way ANOVA was used to measure the effect of genotype and sex on TUNL accuracy and Šídák's multiple comparisons test was used for post-hoc comparisons. For the drug challenge, a repeated measures three-way ANOVA was performed with genotype, sex and treatment as between group factors. Sphericity was assumed for each test. There was no main effect of sex or interaction of sex with genotype or drug effects, therefore male and female data was consolidated and multiple comparisons tests were used as recommended by GraphPad. As fewer females performed the task to criterion while tethered, and there was no interaction of sex with genotype or drug for TUNL accuracy, data from males and females were combined for electrophysiological analysis. Two-way repeated measures ANOVA was used to measure the effect of genotype and treatment on ongoing neural oscillatory changes and Šídák's multiple comparisons test was used for post-hoc comparisons. For the task-evoked oscillatory changes, a repeated measures two-way ANOVA was performed with response and time as between factors to determine change in power according to whether mice chose the correct or incorrect response, and over the time course of the task. Sphericity was assumed for each test. Two-way ANOVAs were then performed for WT and KO mice to assess the impact of drug and response for each genotype. Outliers were removed by means of the ROUT test (Q = 5%). In all cases, the significance level was set to p ≤ 0.05. Power analysis for 80% power using the 3-way ANOVA design requires n = 8 for a medium effect (0.75). RESULTS PCP does not disrupt working memory in GluN2D-KO mice in contrast to other NMDAR antagonists Mice were trained until they reached a criterion of 80% accuracy on the TUNL task over 3 consecutive days. Once they reached this criterion they were then challenged 10 min prior to the task with either saline, PCP, MK-801, R-ket or S-ket. Figure 1 A shows a significant effect of PCP (F (1, 79) = 5.395, p = 0.023), a significant effect of genotype (F (1, 79) = 9.126, p = 0.003) and a significant PCP x genotype interaction (F (1, 79) = 4.673, p = 0.034) on working memory accuracy. Here, Šídák's multiple comparisons test showed a significant difference between WT and KO mice only in the PCP treated group (p = 0.001), not in saline treated mice (p = 0.79) and PCP disrupted working memory accuracy in WT (p = 0.003) but not GluN2D-KO mice compared to saline (p = 0.99) (Fig. 1 A). MK-801 disrupted working memory in both genotypes as seen by a main effect of MK-801 (F (1, 80) = 95.35, p < 0.0001) but no effect of genotype and no genotype x drug interaction (Fig. 1 B). For R-ket and S-ket (Fig. 1 C&D) there was no effect of drug, genotype or interaction when assessing all 48 trials of the TUNL task. However, when assessing the first 12 trials (approximately first 15 min of the test) we found a main effect of drug for both R-ket (F (1, 80) = 17.51, p < 0.0001) and S-ket (F (1, 80) = 7.617, p = 0.007). However, there was no effect of genotype and no genotype x drug interaction. Therefore, all NMDAR antagonist drugs disrupt working memory, however, for PCP this is only true in WT, not GluN2D-KO mice. PCP does not increase baseline hippocampal gamma power in GluN2D-KO mice in contrast to other NMDAR antagonists Baseline gamma power was calculated as the average power recorded across all intertrial intervals within the TUNL task – a period whereby mice were in the touchscreen chambers but not actively performing the task. In the hippocampus, there was a main effect of PCP (F (1, 25) = 7.111, p = 0.013), genotype (F (1, 29) = 4.715, p = 0.038) and a drug x genotype interaction (F (1, 25) = 4.553, p = 0.043) on gamma power (Fig. 2 A &B ). Here, PCP increased baseline gamma power but only in WT mice (p = 0.0015), not in GluN2D-KO mice (p = 0.92). This difference between WT and GluN2D-KO mice was therefore only present in PCP-treated mice (p = 0.008), not saline-treated mice (p = 0.97) (Fig. 2 B). For low gamma, however, there was a main effect of genotype (F (1, 29) = 5.826, p = 0.02) but no effect of PCP or interaction (Fig. 2 C). Here, GluN2D-KO mice show reduced low gamma in the dorsal hippocampus at baseline. MK-801 increased baseline gamma power in the hippocampus independent of genotype (main effect of drug (F (1, 53) = 13.99, p = 0.0005, Fig. 2 D &E )), but had no effect on low gamma (Fig. 2 F). R-ket had no significant effect on baseline gamma or low gamma in the dorsal hippocampus (Fig. 2 G,H,I). However, there was a main effect of genotype for low gamma, similar to the PCP groups, whereby GluN2D-KO mice show reduced low gamma at baseline (F (1, 30) = 6.617, p = 0.015). S-ket increased baseline hippocampal gamma power (F (1, 26) = 5.918, p = 0.02), independent of genotype (Fig. 2 J, K), but had no effect on low gamma (Fig. 2 L). In the PFC, there was a significant effect of PCP (F (1, 51) = 41.90, p < 0.0001), genotype (F (1, 51) = 13.70, p = 0.0005) and a PCP x genotype interaction (F (1, 51) = 4.235, p = 0.04) for baseline gamma power (Fig. 3 A &B ). Here, both WT and KO mice show increased baseline gamma power following PCP treatment, however this effect is heightened in GluN2D-KO mice with the significant difference between WT and KO only apparent when treated with PCP (p = 0.0003), but not saline (Fig. 3 A &B ). PCP also increased low gamma in the PFC independent of genotype (main effect of drug, F (1, 22) = 25.49, p < 0.0001, Fig. 3 C). MK-801 (F (1, 45) = 62.12, p < 0.0001, Fig. 3 D,E), R-ket (F (1, 50) = 8.083, p = 0.006, Fig. 3 G,H) and S-ket (F (1, 21) = 37.58, p < 0.0001, Figure J,K ) all increased baseline gamma power in the PFC, independent of genotype. However, in the R-ket and S-ket groups there was also a main effect of genotype with GluN2D-KO mice showing increased gamma power compared to WT (Fig. 3 H, effect of genotype, F (1, 50) = 8.592, p = 0.005; Fig. 3 K, effect of genotype, F (1, 31) = 6.843, p = 0.03). Low gamma power in the PFC was increased by MK-801(F (1, 46) = 42.75, p < 0.0001, Fig. 3 F), R-ket (F (1, 22) = 4.821, p = 0.04, Fig. 3 I) and S-ket (F (1, 22) = 22.76, p < 0.0001, Fig. 3 L) with no effect of genotype or interaction with genotype. Task-induced change in low gamma power increased during maintenance when mice chose the correct response We next sought to determine if task-induced change in gamma or low gamma differed across three distinct phases of the task (encoding, maintenance and retrieval, Fig. 4 A) according to whether the mouse chose the correct or incorrect response in the choice phase. Spectral power heat maps were generated and we observed an induced low gamma signal specifically during the maintenance phase and specifically within the hippocampus, which appeared to be higher when mice made the correct choice compared to incorrect choice (Fig. 4 B). We next assessed and statistically compared change in low gamma power within the hippocampus in all mice (irrespective of treatment or genotype) across the 2 s of encoding, maintenance and retrieval and according to whether they selected the correct or incorrect choice. Change in low gamma power significantly increased across the 2 s maintenance period (main effect of time, F (2.797, 727.3) = 10.24, p < 0.0001) and was significantly increased during the maintenance period if the mouse chose the correct response compared to incorrect response (main effect of response, F (1, 260) = 5.148, p = 0.024, Fig. 4 D) but low gamma was not altered by time or response choice during encoding, or retrieval (Fig. 4 C &E ). Change in low gamma was unaltered by correct or incorrect response during all three phases of encoding, maintenance and retrieval in the PFC (data not shown). We next sought to determine if genotype or drug influence change in hippocampal low gamma during the maintenance phase given change in low gamma was increased when mice made the correct compared to incorrect selection. Here, the later 1.5-2 s of the maintenance phase was assessed as this is the time point whereby the largest difference between correct and incorrect response can be found (Fig. 4 D). In WT mice, MK-801 significantly reduced low gamma power during the later maintenance phase, irrespective of whether the mouse chose the correct or incorrect response (main effect of drug, F (1, 44) = 4.122, p = 0.04) (Fig. 4 F). No other drugs had a significant effect on low gamma power during this phase. In KO mice, there was no significant effect of any drugs on low gamma power during the maintenance phase (Fig. 4 G). DISCUSSION This study expectedly found that NMDAR antagonists consistently impair working memory; however, the effect of PCP was only present in WT, but not GluN2D KO mice, suggesting this receptor subunit is required for the action of PCP in WM. Furthermore, all drugs increased baseline gamma and/or low gamma power in the hippocampus and PFC to varying degrees, however, once again, for PCP this was genotype specific. In the hippocampus, PCP increased gamma power only in WT mice, not KO mice. In contrast, the effects of PCP on gamma power in PFC were exacerbated in KO mice. Finally, we identified upregulation of task-induced low gamma oscillations during the maintenance phase of the TUNL task when mice chose the correct response in the task. Finally, MK-801 significantly reduced low gamma activity during this maintenance period in WT but not KO mice. We report no differences between the genotypes in TUNL task accuracy. A recent study assessing conditional KO of the GluN2D subunit from PV interneurons similarly reported no impairments in PV-GluN2D-KO mice during the Y-maze task of spatial WM when compared with WT mice 54 . However, they found impairments in short-term but not long-term memory during the Novel Object Recognition task and significant deficits in cognitive flexibility during the water T-maze test 54 . These cognitive measures were not assessed in our study. However, it suggests that while deletion of the GluN2D subunit is not sufficient to cause WM impairments, the GluN2D subunit may be important for other cognitive functions such as object recognition memory and cognitive flexibility. In this study, we show diverging effects of the NMDAR antagonists PCP, MK-801, R-ket and S-ket on TUNL performance. Treatment with PCP leads to a decrease in overall accuracy in WT but not GluN2D-KO mice, suggesting that the WM-impairing effects of PCP are mediated by the GluN2D subunit. This differential drug response may be explained by the relative affinity of each drug for specific NMDAR subunits. For example, previous reports suggest that PCP is least potent for GluN2A but shows comparable potency for GluN2B-D subunits, while MK-801 is 10-fold more potent for GluN2A or B containing receptors 55 , 56 , and ketamine has a higher potency at GluN1/2C as well as GluN1/2B subunit containing receptors 57 , 58 . Previous reports suggest that PV interneurons are particularly affected by PCP treatment, with prenatal PCP treatment selectively reducing PV density in the PFC and hippocampus, and these changes are linked with schizophrenia-like behavioural deficits including WM impairment 59 – 61 . Our results suggest that PCP in disrupting WM primarily acts through GluN2D-containing NMDARs, which are mainly expressed on PV interneurons, altering the E/I balance in the brain 62 . In contrast, MK-801 impaired overall accuracy in both genotypes which suggests that WM deficits following MK-801 are not mediated by the GluN2D subunit. A previous study showed that MK-801 dose-dependently impaired TUNL accuracy in both WT mice and mice lacking the obligatory GluN1 subunit of NMDARs from PV interneurons and forebrain pyramidal cells 63 . This suggests that NMDAR hypofunction on interneurons may not be the primary mechanism underlying the WM deficits following MK-801 treatment. Alternatively, studies have linked the cholinergic and dopaminergic systems as well as changes in glial cells like astrocytes to the WM-inducing effects of MK-801 64–66 . R-ket and S-ket did not impact accuracy in either genotype when considering effects over the entire session, however when examining accuracy over the first 12 trials alone, a significant reduction in accuracy was observed. This may reflect the shorter half-life of ketamine when compared with PCP or MK-801. The elimination half-life of ketamine is thought to be approximately 13 min in mice when administered i.p. compared with 46 min for PCP, whilst MK-801 has been shown to persist for 3 h following a single administration 67 – 69 . All NMDAR antagonists increased baseline gamma power in the prefrontal cortex and to varying degress in the hippocampus. This aligns with the increase in baseline gamma power reported in people with schizophrenia 70 and in previous animal model studies showing acute treatment with NMDAR antagonists increases gamma power in both the hippocampus and frontal cortex 27 , 71 – 73 . We extend this work to show novel findings that PCP only increases baseline gamma power in the hippocampus of WT mice, and GluN2D-KO mice are protected from this increase in baseline gamma power. Furthermore, the effect of PCP was also influenced by genotype in the PFC, but in an opposite manner, whereby GluN2D-KO mice show a heightened baseline gamma power increase in response to PCP. These genotype specific effects of PCP on gamma power may be an underlying mechanism by which GluN2D- KO mice are protected from the WM deficits induced by PCP. Previously, Sapkota et al., reported that ketamine induced a much larger increase in high gamma power in WT compared with GluN2D-KO mice and suggested that the GluN2D subunit may be critical for ketamine’s effect on neural oscillations 44 . Our data showed a main effect of genotype on low gamma power in the hippocampus for the R-ket treated group whereby WT mice showed a larger increase in gamma power in response to both saline and R-ket than GluN2D KO, however, within the PFC GluN2D-KO mice seemed to show a heightened response in both R-ket and S-ket groups. A major point of difference here is that we used depth electrodes and show striking regional differences, while Sapkota et al. used electrocorticographic analysis. Our data indicate differential effects of PCP depending both on genotype and region, and show that hippocampal changes in baseline gamma power align with working memory performance. Task-induced changes in gamma power were evident during the maintenance phase of the task and specifically within the low gamma frequency range (30-40Hz). Low gamma during maintenance was significantly higher when mice went on to make the correct response in the choice phase, suggesting this is an electrophysiological correlate of working memory maintenance. In addition, this was significantly dampened by MK-801. This aligns with previous reports that people with schizophrenia show reduced task-induced gamma power specifically during the maintenance phase of WM 74 . However, the study by Haenshel et al. also reported reduced theta and gamma activity during the retrieval phase in people with schizophrenia 74 and we did not find any significant effect of NMDAR antagonists on induced gamma power during retrieval. This highlights an important difference between drug-induced models and people with schizophrenia. A previous study in mice found that MK-801 disrupts theta-gamma co-modulation during the Y-maze spontaneous alternation task 75 , however, to the best of our knowledge, the effects of MK-801 on gamma or theta power during specific phases of WM in mice have not been previously reported. Given MK-801 showed the strongest behavioural effect on WM accuracy, it is not suprising that this was the only drug to cause a significant reduction in task-induced low gamma power. In conclusion, we report here that NMDAR antagonists disrupt WM accuracy, and characterise electrophysiological changes in the hippocampus and PFC which accompany these behavioural consequences of these drugs. We also add that PCP acts through the GluN2D subunit to exert its effects on baseline hippocampal gamma power and WM accuracy. Furthermore, we report here that the TUNL WM task induced a low gamma signal during the maintenance phase of the task and this signal was increased when mice went on to make the correct response in the choice phase, suggesting it is an important parameter in the ability of the mouse to complete the task correctly. MK-801 disrupted low gamma during the maintenance phase of the task, and showed the strongest effect in disrupting WM, again suggesting a critical link between low gamma during the maintenance phase and WM outcomes. Given the strong link between NMDAR hypofunction and schizophrenia and the burgeoning literature surrounding the specific role of the GluN2D subunit in schizophrenia 76 , these data provide new and important insights into the molecular biology that may underlie working memory disruptions in schizophrenia. Declarations Acknowledgements The authors would like to thank the International Society of Neurochemistry for providing financial support for this study as well as the National Health and Medical Research Council of Australia, GNT2000893 for Rachel Hill. Conflicts of interest The authors declare no conflicts of interst. Data availability statement The authors are open to sharing data upon request. Author contributions CV performed all live animal experiments, and performed data analysis and wrote the manuscript. MH assisted in surgical procedures, data collection and analysis and manuscript editing. KI provided the GluN2D-KO mice and contributed to manuscript editing. SI provided the GluN2D-KO mice and contributed to manuscript editing. MM provided the GluN2D-KO mice and contributed to manuscript editing. SS contributed to concerptualisation and manuscript editing. NCJ contributed to conceptualisation, study design, data collection, data analysis and manuscript editing. RAH conceptualised the study, assisted in study design, obtained funding for the study, assisted with data analysis and assisted in manuscript writing and editing. References McGrath J, Saha S, Chant D, Welham J. Schizophrenia: a concise overview of incidence, prevalence, and mortality. Epidemiologic reviews 2008; 30: 67–76. Solmi M, Seitidis G, Mavridis D, Correll CU, Dragioti E, Guimond S et al. Incidence, prevalence, and global burden of schizophrenia - data, with critical appraisal, from the Global Burden of Disease (GBD) 2019. Molecular psychiatry 2023; 28(12): 5319–5327. Network. GBoDC. 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Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files SupplementaryFigure1.docx SupplementaryTable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5412811","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":375736042,"identity":"916f093b-c968-4741-9242-fa72161f2b4c","order_by":0,"name":"Rachel Hill","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYHACNgaGAoYENnbmxgdgvgQQ8xDUYgDUwszYbECaFgZmxjYJorSYtx9+9uCDAUMeH1BL1Y2KO/LysxsYH7xtw61F5kyaueEMA4ZioMPabueceWa44c4BZsO5eLRIMOSwSfMYMCS2gbTkth1m3CCRwCbNi08L/xs26T9QLcVALfbzZySw/8arRQJoCwNUCzNQS2LDDWDo4dfyzEyyx0ACpKVZOufM4eQNNxKbJeecw+ew5GcSPypsEue3Nx/8nFNx2Hb+jOSDH96U4dYCDwUkwNhAUP0oGAWjYBSMAvwAADfgSmlDyIn/AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6111-355X","institution":"Monash University","correspondingAuthor":true,"prefix":"","firstName":"Rachel","middleName":"","lastName":"Hill","suffix":""},{"id":375736043,"identity":"d6761c35-b35d-4c0d-9477-d4ecf08e37d2","order_by":1,"name":"Chitra Vinnakota","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chitra","middleName":"","lastName":"Vinnakota","suffix":""},{"id":375736044,"identity":"0a88e0d6-cc16-4cbc-8c64-eef29c35796d","order_by":2,"name":"Matthew Hudson","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Hudson","suffix":""},{"id":375736045,"identity":"f2bf412a-f939-466c-9602-901874138f3c","order_by":3,"name":"Kazutaka Ikeda","email":"","orcid":"https://orcid.org/0000-0001-8342-0278","institution":"Tokyo Metropolitan Institute of Medical Science","correspondingAuthor":false,"prefix":"","firstName":"Kazutaka","middleName":"","lastName":"Ikeda","suffix":""},{"id":375736046,"identity":"185b9c1e-a239-428a-9fbe-ef5f5319b4d1","order_by":4,"name":"Soichiro Ide","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Soichiro","middleName":"","lastName":"Ide","suffix":""},{"id":375736047,"identity":"9d6c424b-c4f0-450a-a814-fac51a99eba0","order_by":5,"name":"Masayoshi Mishina","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Masayoshi","middleName":"","lastName":"Mishina","suffix":""},{"id":375736048,"identity":"d64ac042-1b0e-4ef4-b731-d022c9af8efb","order_by":6,"name":"Suresh Sundram","email":"","orcid":"https://orcid.org/0000-0002-9674-0227","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Suresh","middleName":"","lastName":"Sundram","suffix":""},{"id":375736049,"identity":"59f43eb1-9250-4c3e-b75a-0896d50bf234","order_by":7,"name":"Nigel Jones","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nigel","middleName":"","lastName":"Jones","suffix":""}],"badges":[],"createdAt":"2024-11-08 01:45:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5412811/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5412811/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71728164,"identity":"c3c16d83-ace1-4aeb-9867-ba3ef2f196d4","added_by":"auto","created_at":"2024-12-18 06:28:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3356990,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eWorking memory accuracy in WT (solid bars) and GluN2D KO (patterned bars) mice administered PCP (A), MK-801 (B), R-ket (C) or S-ket (D) over 48 trials. Working memory accuracy over the first 12 trials following R-ket (E) and S-ket (F) administration. All drugs impair working memory accuracy, however, for PCP this was only observed in WT mice, not KO mice. Also, for R-ket and S-ket, disruptions to working memory are only observed during the first 12 trials. n=20-22 mice/group. Data are mean ± SEM. **p\u0026lt;0.01, ***p\u0026lt;0.0001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5412811/v1/594db87e935b816126bacb49.png"},{"id":71728757,"identity":"bc9e9189-343f-480d-9eee-d468f7f460d2","added_by":"auto","created_at":"2024-12-18 06:36:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14421608,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBaseline hippocampal gamma and low gamma power\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Power spectral density plots of PCP (A), MK801 (D), r-ket (G) and s-ket (J) occurring in hippocampus of WT and GluN2D- KO mice. PCP increased hippocampal gamma power (B) in WT but not GluN2D-KO mice, while GluN2D-KO mice show decreased hippocampal low gamma power (C). MK-801 increased baseline gamma power (E) but had no effect on low gamma (F). R-ket had no significant effect on baseline hippocampal gamma or low gamma (I). S-ket increased baseline gamma (K) but had no effect on low gamma (L). n=11-18/group. Data are Mean ± SEM. *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.0001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5412811/v1/e466f5b8f1f4ec4c59df969c.png"},{"id":71728756,"identity":"7ad83bfb-5470-49e4-acdc-0735be3bb023","added_by":"auto","created_at":"2024-12-18 06:36:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16013872,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBaseline PFC gamma and low gamma power. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003ePower spectral density plots of PCP (A), MK801 (D), r-ket (G) and s-ket (J) occurring in hippocampus of WT and GluN2D-KO mice. PCP increased baseline gamma (B), and low gamma (C) and this effect of PCP was exacerbated in KO mice. MK-801 increased gamma (E) and low gamma (F) in both genotypes. R-ket increased gamma (H) and low gamma (I) in both genotypes. S-ket increased gamma (K) and low gamma (L) in both genotypes in the prefrontal cortex. n=11-18/group. Data are Mean ± SEM. *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.0001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5412811/v1/849b603e390e1d2828d98434.png"},{"id":71728170,"identity":"13d2136f-cabe-4a2c-8885-ff2712c417bd","added_by":"auto","created_at":"2024-12-18 06:28:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":19232151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eA.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Schematic illustration depicting the inter-trial interval from which baseline recordings are captured and the sample and choice phases of the TUNL task. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Spectral power heat map of all trials from 5-50Hz in all three phases of the TUNL task relative to baseline periods; The top panel represent data from correctly answered trials, whereas the bottom panel represents data from incorrect trials. The thin dotted line represents the time at which a mouse completes the encoding phase and initiates the maintenance phase. The thick black line represents a variable duration of time, reflective of variable maintenance phase duration across trials. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eC.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e During the encoding phase low gamma is unchanged by time or by response type. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eD.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eDuring the maintenance phase low gamma increases as mice are about to select the choice phase selection and in addition low gamma power is increased when mice go on to make the correct choice. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eE.\u003c/strong\u003e\u003c/em\u003e \u003cem\u003eDuring the retrieval phase low gamma is unchanged by time or by correct or incorrect response. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Change in hippocampal low gamma during the maintenance phase was significantly reduced in WT mice by MK-801, but not PCP, S-ket or R-ket \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. No effect of drug was found for KO mice n=11-18/group. Data are Mean ± SEM. *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.0001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5412811/v1/2cc05296e897a43fa1f5e84c.png"},{"id":71728168,"identity":"186fa1dd-bb0f-4993-baa1-c1e0e58fcea6","added_by":"auto","created_at":"2024-12-18 06:28:37","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":39234,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5412811/v1/dd0b47da05f0241c838cc177.docx"},{"id":71728166,"identity":"ffc8aa61-fcd9-402b-bc20-4b5963253360","added_by":"auto","created_at":"2024-12-18 06:28:37","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":13554,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5412811/v1/7e1f980c48fdaf7a4205da7f.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Differential effects of NMDAR antagonists on working memory and gamma oscillations, and the mediating role of the GluN2D subunit","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSchizophrenia affects 0.29\u0026ndash;0.7% \u003csup\u003e1, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of the world\u0026rsquo;s population but causes significant social and economic burden with disability-adjusted life years estimated at 15.1\u0026nbsp;million \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Approximately 80% of individuals with schizophrenia are unemployed and it is one of the top 20 causes of years lived with disability globally\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The economic impact of schizophrenia is estimated between 0.30\u0026ndash;0.60% of GDP in high income countries incorporating both direct medical treatment costs as well as indirect economic loss related to criminal justice/homelessness, loss of tax revenue, and productivity losses for both people with schizophrenia and their caregivers \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. While schizophrenia is characterised by positive symptoms that \u0026ldquo;add\u0026rdquo; to one\u0026rsquo;s psyche, including delusions and hallucinations, as well as negative symptoms that \u0026ldquo;take away\u0026rdquo; from one\u0026rsquo;s psyche, such as social withdrawal, anhedonia and disordered thinking, cognitive symptoms are the best predictor of functional outcomes such as employment, independent living \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e \u0026ndash; outcomes that carry significant financial burden. Cognitive symptoms include impairments in working memory, verbal learning, visual learning, attention, reasoning and problem solving and social cognition \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, working memory (WM) in particular has been specifically related to long-term community functioning and as such has been identified as a core feature of schizophrenia \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Importantly, WM deficits are not treated by current antipsychotic medications, therefore there is an urgent need to better understand the mechanisms causing WM impairments in schizophrenia in order to develop evidence-based treatments.\u003c/p\u003e \u003cp\u003eNeural oscillations play a crucial role in facilitating information processing and communication within and across brain regions\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Gamma oscillations (30-100Hz), in particular, are implicated in higher-order cognitive and sensory processing \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and are evoked when humans perform tasks that require WM, attention, emotional processing and perception \u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In people with schizophrenia increased baseline (or ongoing) gamma power but reduced cognitive task-induced gamma power, compared with healthy humans has been reported \u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. These aberrant changes in both resting-state and induced gamma oscillations have been linked to cognitive symptoms in people with schizophrenia \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. For example, one study reported a significant negative correlation between resting-state gamma power and performance of a verbal learning task in people with schizophrenia \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, while another reported significantly reduced gamma oscillations during a WM task in people with schizophrenia compared with controls \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Understanding neural oscillation dysfunction in schizophrenia may provide clues as to how to better treat WM impairment.\u003c/p\u003e \u003cp\u003eNMDAR antagonists are well known to recapitulate the full spectrum of behavioural symptoms relevant to schizophrenia, and have therefore been used as tools to model schizophrenia symptoms in rodents \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Interestingly, NMDAR antagonists evoke similar gamma oscillatory changes to those found in schizophrenia. NMDAR antagonists like ketamine, PCP and MK-801 have repeatedly been shown to increase ongoing gamma power in the cortex and hippocampus and decrease stimulus-evoked gamma power in humans and rodent models \u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30 CR31 CR32\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The oscillatory and behavioural effects of these drugs promote the acute NMDAR antagonist model as one which can be used to explore the interrelationships between behaviour and electrophysiology relating to schizophrenia \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough the precise biophysical and cellular mechanisms underlying neuronal oscillatory activity are not clear, there is extensive evidence implicating fast-spiking PV interneurons in the generation of neural oscillations within the gamma frequency range \u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. PV interneurons receive NMDAR-mediated excitatory input from pyramidal cells and in turn modulate neural network activity via synchronous and co-ordinated GABAergic inhibition of local excitatory neurons, resulting in gamma oscillations \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. One leading theory for the aberrant gamma oscillatory changes in schizophrenia is the hypofunction of NMDARs on PV interneurons \u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. As GluN2D-containing NMDARs are especially enriched in PV interneurons \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, and there is a reported reduction in the ketamine-induced increase in baseline gamma power in GluN2D-knockout (KO) mice compared with WT mice \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, the objective of this study was to explore NMDAR antagonist-induced changes in neuronal oscillations in GluN2D-KO compared with WT mice during the performance of a WM task.\u003c/p\u003e \u003cp\u003eThere are several different methods to assess cognition in animal models. However, many of these methods bear little resemblance to how cognition is assessed in humans, which may contribute to the current lack of effective therapies for cognitive symptoms in schizophrenia. The Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) initiative was established in response to this need for therapies that improve functional outcomes in patients \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. One of the main goals of CNTRICS was the development of tasks with a high degree of cognitive construct validity which could be used both in humans and in animal models. The rodent operant touchscreen system was thus created with the aim of closely resembling assessments of cognition used clinically to improve translatability and maximise efficiency in identifying appropriate therapeutic targets and treatments for schizophrenia \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The touchscreen system is translational, automated, non-aversive, low-stress, able to assess multiple cognitive domains within the same testing environment and can detect both impairments and enhancements in function \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. This study utilises the trial-unique non-match to location (TUNL) touchscreen-based task to assess the influence of NMDAR antagonists on working memory in wild type (WT) and GluN2D-KO mice. In addition, for the first time, we combine touchscreen testing with simultaneous measurement of electrophysiological signals during task performance with the goal of identifying neural oscillation patterns which underpin drug and genotype effects on WM.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimals and housing\u003c/h2\u003e \u003cp\u003eGluN2D-KO mice were obtained from the Tokyo Metropolitan Institute of Medical Science and transported to the Monash Animal Research Platform, Monash Medical Centre (Clayton, Victoria, Australia) where a breeding colony was established. GluN2D heterozygous mice were bred to obtain WT, heterozygous and homozygous GluN2D-KO male and female littermates. At 6\u0026ndash;7 weeks of age, mice were transferred from the breeding facility to the laboratories in the Department of Neuroscience, School of Translational Medicine, Monash University (Prahran, Victoria, Australia) where all husbandry, housing, and behavioral testing was undertaken. All mice (n\u0026thinsp;=\u0026thinsp;42) were housed in groups of 2\u0026ndash;5 in individually ventilated cages (Techniplast, NSW, Australia) with a reversed 12-h dark-light cycle (lights off at 9:30 am) allowing experiments to be conducted during the active phase of the mouse circadian cycle. Cages were monitored daily and changed weekly. After allowing mice to acclimatize to reverse light conditions (2 weeks), a food-restricted diet was gradually introduced (with water available \u003cem\u003ead libitum\u003c/em\u003e), until mice reached 85\u0026ndash;90% of their initial free-feeding weight, which was maintained throughout the testing period. All procedures were approved by the Monash University Animal Ethics Committee (Project #: E/1837/2018/M).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTrial Unique Non-Matching to Location (TUNL) task of working memory\u003c/h3\u003e\n\u003cp\u003eThe TUNL task was performed in the automated touchscreen operant chambers (Campden Instruments Ltd., UK) for mice, and Whisker and Abet II software (Campden Instruments Ltd., UK) were used to control the system and for data collection as previously described \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe TUNL task was conducted as previously described \u003csup\u003e\u003cspan additionalcitationids=\"CR48 CR49\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Briefly, mice habituated to the chambers were sequentially trained to: recognize visual stimuli and associate them with food delivery; nose-poke stimuli to trigger a reward; initiate the next trial by breaking an infrared (IR) beam near the reward collection tray; and lastly, to avoid touches to non-illuminated windows. Mice were advanced to TUNL training upon completing 48 trials within 30 mins at an accuracy above 80% over two consecutive days (see supplementary Table\u0026nbsp;1 for TUNL training protocol).\u003c/p\u003e \u003cp\u003eEach TUNL session consisted of a maximum of 48 trials, each comprising two phases: during the sample phase, an initiation triggered by an IR beam break near the reward collection tray would result in the illumination of one window out of five possible locations. After the mouse nose-poked this stimulus, the stimulus disappears and a varying delay period begins. A second initiation triggers the start of the subsequent (choice) phase where a new location appears alongside the sample location requiring the mice to recall the sample location and choose the new location for a food reward. If the mouse chooses correctly, the delivery of the food reward is followed by an inter-trial interval (ITI) before the next trial. Whereas, a nose-poke to the incorrect location leads to a 5-s timeout and the initiation of a correction trial, where the same stimuli are presented repeatedly until a correct response is achieved. One session was conducted per mouse per day, occurring six days a week except for the pharmacological studies during which there was a minimum gap of 48 h between consecutive treatments. Full training details are outlined in Supplementary Table\u0026nbsp;1. During probe sessions mice were tested using S1c trials with a delay of 1 s between the sample and choice phase as our previous findings indicated that mice tend to use working memory rather than other strategies, like side bias, when tested using this configuration \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eDrug challenge\u003c/h3\u003e\n\u003cp\u003eMice were injected with either vehicle (0.9% saline) or S-ketamine (S-ket) (30 mg/kg), R-ketamine (R-ket) (30 mg/kg), PCP (1 mg/kg) or MK-801 (0.3 mg/kg) as previously described\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. All mice randomly received each dose of all treatments with at least 48 h in between treatments. Each compound was delivered via a single intraperitoneal (i.p.) injection at 10 \u0026micro;l/g. Drugs were administered 10 mins before the testing session began for S-ket, R-ket and PCP and 30 mins before the testing session for MK-801. The primary outcome measure was task accuracy. During the drug challenge, the experimenter was blinded to the genotype of the mice but not the drug type or dose.\u003c/p\u003e\n\u003ch3\u003eElectrode implantation surgery\u003c/h3\u003e\n\u003cp\u003eFollowing the completion of behavioral experiments, mice underwent surgery to implant recording electrodes in the medial prefrontal cortex (mPFC) and dorsal hippocampus (dHPC) as previously described\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Mice were anaesthetized with 5% isoflurane, and 125 \u0026micro;m stainless steel recording electrodes (Cat # E363/3/SPC, 20 mm, PlasticsOne, Bioscientific, NSW, Australia) were implanted into the mPFC (AP: +1.9, ML: -0.4, DV: -1.7) and dHPC (AP: -1.8, ML: 1.3, DV: -1.3). A ground/reference electrode (Cat # E363/120/2.4, PlasticsOne, Bioscientific, NSW, Australia) was screwed into the cerebellum and two anchor screws were inserted on either side of the frontal plate. Electrodes were then connected to a multi-channel electrode pedestal (Cat # M52-5002545, Element 14, Australia) and secured to the skull using super glue and dental cement. Mice were administered buprenorphine for pain relief at the end of the surgery and allowed to recover for 7 days with food and water available \u003cem\u003ead libitum\u003c/em\u003e.\u003c/p\u003e\n\u003ch3\u003eElectrophysiological procedures\u003c/h3\u003e\n\u003cp\u003eOnce TUNL performance was re-established post-surgery with the head-stage cable attached, mice were exposed to vehicle (0.9% saline), S-ket (30 mg/kg), R-ket (30 mg/kg), PCP (1 mg/kg) or MK-801 (0.3 mg/kg) via a single i.p. injection at 10 \u0026micro;l/g and were tested in TUNL probe sessions where combined electrophysiological and TUNL behavioural data were simultaneously recorded. Whisker and Abet II software (Campden Instruments Ltd., UK) were used to collect behavioral data whilst the electrophysiological data was and acquired using Multi Channel Systems software (Harvard Biosciences Inc., USA). A modified ABET TUNL schedule was used which allowed behavioural data to be synchronised with electrophysiological data. To record continuous LFPs during the test sessions, mice were connected via the head-mounted electrode pedestal to a custom-designed electrophysiology cable within the touchscreen chamber which was responsible for signal conditioning, multiplexing and digitising the analogue electrophysiological signal and transmitting the data to the acquisition hub where it was synchronised with behavioural data. The head stage cable was attached to a dragonfly commutator placed atop the touchscreen enclosure which prevented twisting of the cable when the mouse was performing the touchscreen task. Recordings were performed using Multi-Channel Systems Experimenter software (version # 2.8.2.18079, Harvard Biosciences Inc., USA), and sampled at 2000Hz.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eElectrophysiological analysis\u003c/h2\u003e \u003cp\u003eThe time-stamped electrophysiology data was imported from Neuroexplorer (Plexon, USA) and analysed using custom-designed MATLAB (MathWorks, USA) scripts. Electrophysiology analysis consisted of two measurements: baseline activity and task-induced activity. Data was extracted from the relevant periods (see below for description). All epochs were first visually inspected for artefacts, with any epochs containing appreciable artefact manually rejected from subsequent analysis. While analysing the data, we found a small number of trials with a very long response time (maximal response time: 551.4 s). These longer response times were considered to be significantly beyond the WM capacity of mice. Upon plotting the response time of all trials (Supplementary Fig.\u0026nbsp;1), we found that 75% of trials were completed within 7s and thus, any trial that took greater than 7 s was excluded from further analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement of baseline activity\u003c/h3\u003e\n\u003cp\u003eFirst, we examined the effect of the NMDAR antagonists on baseline oscillatory power in WT and GluN2D-KO mice. For this, we extracted data from a 5s window within the 12s intertrial interval (ITI) preceding each trial. All epochs were subject to spectral analysis using the multitaper method to compute the power spectral density (PSD) between 1 and 200Hz. This was achieved using the mtspectrumc function from the MATLAB Chronux plugin. The PSD estimate was then averaged across all baseline periods within each TUNL session to compute an average PSD. Ongoing power in the theta (5\u0026ndash;10 Hz), beta (20\u0026ndash;30 Hz), low gamma (30\u0026ndash;40 Hz) and gamma (30\u0026ndash;80 Hz) and HFO (100\u0026ndash;200 Hz) frequency band was subsequently calculated by taking the integral of all values within the appropriate frequency interval. To generate power-spectral density plots, data was log transformed for graphical representation.\u003c/p\u003e\n\u003ch3\u003eMeasurement of task-induced activity\u003c/h3\u003e\n\u003cp\u003eWe next measured the effects of NMDAR antagonists on TUNL task-related oscillatory activity in WT and GluN2D-KO mice. Continuous LFP data was segmented into epochs from 0-2000 ms prior to selecting the sample stimulus (i.e.: the encoding phase), 0-2000 ms immediately following selection of the sample stimulus (i.e.: maintenance phase) and 0-2000 ms prior to selection of the choice stimulus (i.e.: retrieval phase) - see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. Epochs were also categorised according to whether the mouse made a correct or incorrect choice in that specific trial. Then a time-frequency analysis was performed whereby the data was subjected to morlet wavelet decomposition using the EEGLab newtimef function in MATLAB. This calculated event related spectral perturbations (ERSPs) at 180 linearly spaced frequencies from 5\u0026ndash;50 Hz with wavelet cycles increasing from 3 to 10. ERSPs indicated a task-evoked substantial increased power within the low gamma (30-40Hz) frequency band specifically during the maintenance phase of the task, therefore power at this frequency was extracted and represented as the change in power relative to the baseline period described above. Theta and gamma activity were also extracted and analysed through each phase of the task but power did not differ between correct and incorrect trials, therefore no further drug or genotype effect analysis was applied\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses and graphical representations were generated using GraphPad Prism (version 8.3.1, GraphPad Software, San Diego). A two-way ANOVA was used to measure the effect of genotype and sex on TUNL accuracy and Š\u0026iacute;d\u0026aacute;k's multiple comparisons test was used for \u003cem\u003epost-hoc\u003c/em\u003e comparisons. For the drug challenge, a repeated measures three-way ANOVA was performed with genotype, sex and treatment as between group factors. Sphericity was assumed for each test. There was no main effect of sex or interaction of sex with genotype or drug effects, therefore male and female data was consolidated and multiple comparisons tests were used as recommended by GraphPad.\u003c/p\u003e \u003cp\u003eAs fewer females performed the task to criterion while tethered, and there was no interaction of sex with genotype or drug for TUNL accuracy, data from males and females were combined for electrophysiological analysis. Two-way repeated measures ANOVA was used to measure the effect of genotype and treatment on ongoing neural oscillatory changes and Š\u0026iacute;d\u0026aacute;k's multiple comparisons test was used for post-hoc comparisons. For the task-evoked oscillatory changes, a repeated measures two-way ANOVA was performed with response and time as between factors to determine change in power according to whether mice chose the correct or incorrect response, and over the time course of the task. Sphericity was assumed for each test. Two-way ANOVAs were then performed for WT and KO mice to assess the impact of drug and response for each genotype. Outliers were removed by means of the ROUT test (Q\u0026thinsp;=\u0026thinsp;5%). In all cases, the significance level was set to p\u0026thinsp;\u0026le;\u0026thinsp;0.05. Power analysis for 80% power using the 3-way ANOVA design requires n\u0026thinsp;=\u0026thinsp;8 for a medium effect (0.75).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePCP does not disrupt working memory in GluN2D-KO mice in contrast to other NMDAR antagonists\u003c/h2\u003e \u003cp\u003eMice were trained until they reached a criterion of 80% accuracy on the TUNL task over 3 consecutive days. Once they reached this criterion they were then challenged 10 min prior to the task with either saline, PCP, MK-801, R-ket or S-ket. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA shows a significant effect of PCP (F (1, 79)\u0026thinsp;=\u0026thinsp;5.395, p\u0026thinsp;=\u0026thinsp;0.023), a significant effect of genotype (F (1, 79)\u0026thinsp;=\u0026thinsp;9.126, p\u0026thinsp;=\u0026thinsp;0.003) and a significant PCP x genotype interaction (F (1, 79)\u0026thinsp;=\u0026thinsp;4.673, p\u0026thinsp;=\u0026thinsp;0.034) on working memory accuracy. Here, Š\u0026iacute;d\u0026aacute;k's multiple comparisons test showed a significant difference between WT and KO mice only in the PCP treated group (p\u0026thinsp;=\u0026thinsp;0.001), not in saline treated mice (p\u0026thinsp;=\u0026thinsp;0.79) and PCP disrupted working memory accuracy in WT (p\u0026thinsp;=\u0026thinsp;0.003) but not GluN2D-KO mice compared to saline (p\u0026thinsp;=\u0026thinsp;0.99) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). MK-801 disrupted working memory in both genotypes as seen by a main effect of MK-801 (F (1, 80)\u0026thinsp;=\u0026thinsp;95.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) but no effect of genotype and no genotype x drug interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). For R-ket and S-ket (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u0026amp;D) there was no effect of drug, genotype or interaction when assessing all 48 trials of the TUNL task. However, when assessing the first 12 trials (approximately first 15 min of the test) we found a main effect of drug for both R-ket (F (1, 80)\u0026thinsp;=\u0026thinsp;17.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and S-ket (F (1, 80)\u0026thinsp;=\u0026thinsp;7.617, p\u0026thinsp;=\u0026thinsp;0.007). However, there was no effect of genotype and no genotype x drug interaction. Therefore, all NMDAR antagonist drugs disrupt working memory, however, for PCP this is only true in WT, not GluN2D-KO mice.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePCP does not increase baseline hippocampal gamma power in GluN2D-KO mice in contrast to other NMDAR antagonists\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBaseline gamma power was calculated as the average power recorded across all intertrial intervals within the TUNL task \u0026ndash; a period whereby mice were in the touchscreen chambers but not actively performing the task. In the hippocampus, there was a main effect of PCP (F (1, 25)\u0026thinsp;=\u0026thinsp;7.111, p\u0026thinsp;=\u0026thinsp;0.013), genotype (F (1, 29)\u0026thinsp;=\u0026thinsp;4.715, p\u0026thinsp;=\u0026thinsp;0.038) and a drug x genotype interaction (F (1, 25)\u0026thinsp;=\u0026thinsp;4.553, p\u0026thinsp;=\u0026thinsp;0.043) on gamma power (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e\u0026amp;B\u003c/b\u003e). Here, PCP increased baseline gamma power but only in WT mice (p\u0026thinsp;=\u0026thinsp;0.0015), not in GluN2D-KO mice (p\u0026thinsp;=\u0026thinsp;0.92). This difference between WT and GluN2D-KO mice was therefore only present in PCP-treated mice (p\u0026thinsp;=\u0026thinsp;0.008), not saline-treated mice (p\u0026thinsp;=\u0026thinsp;0.97) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). For low gamma, however, there was a main effect of genotype (F (1, 29)\u0026thinsp;=\u0026thinsp;5.826, p\u0026thinsp;=\u0026thinsp;0.02) but no effect of PCP or interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Here, GluN2D-KO mice show reduced low gamma in the dorsal hippocampus at baseline.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMK-801 increased baseline gamma power in the hippocampus independent of genotype (main effect of drug (F (1, 53)\u0026thinsp;=\u0026thinsp;13.99, p\u0026thinsp;=\u0026thinsp;0.0005, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e\u0026amp;E\u003c/b\u003e)), but had no effect on low gamma (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). R-ket had no significant effect on baseline gamma or low gamma in the dorsal hippocampus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eG,H,I). However, there was a main effect of genotype for low gamma, similar to the PCP groups, whereby GluN2D-KO mice show reduced low gamma at baseline (F (1, 30)\u0026thinsp;=\u0026thinsp;6.617, p\u0026thinsp;=\u0026thinsp;0.015). S-ket increased baseline hippocampal gamma power (F (1, 26)\u0026thinsp;=\u0026thinsp;5.918, p\u0026thinsp;=\u0026thinsp;0.02), independent of genotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ, K), but had no effect on low gamma (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eL).\u003c/p\u003e \u003cp\u003eIn the PFC, there was a significant effect of PCP (F (1, 51)\u0026thinsp;=\u0026thinsp;41.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), genotype (F (1, 51)\u0026thinsp;=\u0026thinsp;13.70, p\u0026thinsp;=\u0026thinsp;0.0005) and a PCP x genotype interaction (F (1, 51)\u0026thinsp;=\u0026thinsp;4.235, p\u0026thinsp;=\u0026thinsp;0.04) for baseline gamma power (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e\u0026amp;B\u003c/b\u003e). Here, both WT and KO mice show increased baseline gamma power following PCP treatment, however this effect is heightened in GluN2D-KO mice with the significant difference between WT and KO only apparent when treated with PCP (p\u0026thinsp;=\u0026thinsp;0.0003), but not saline (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e\u0026amp;B\u003c/b\u003e). PCP also increased low gamma in the PFC independent of genotype (main effect of drug, F (1, 22)\u0026thinsp;=\u0026thinsp;25.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). MK-801 (F (1, 45)\u0026thinsp;=\u0026thinsp;62.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eD,E), R-ket (F (1, 50)\u0026thinsp;=\u0026thinsp;8.083, p\u0026thinsp;=\u0026thinsp;0.006, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eG,H) and S-ket (F (1, 21)\u0026thinsp;=\u0026thinsp;37.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, \u003cb\u003eFigure J,K\u003c/b\u003e) all increased baseline gamma power in the PFC, independent of genotype. However, in the R-ket and S-ket groups there was also a main effect of genotype with GluN2D-KO mice showing increased gamma power compared to WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eH, effect of genotype, F (1, 50)\u0026thinsp;=\u0026thinsp;8.592, p\u0026thinsp;=\u0026thinsp;0.005; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eK, effect of genotype, F (1, 31)\u0026thinsp;=\u0026thinsp;6.843, p\u0026thinsp;=\u0026thinsp;0.03). Low gamma power in the PFC was increased by MK-801(F (1, 46)\u0026thinsp;=\u0026thinsp;42.75, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), R-ket (F (1, 22)\u0026thinsp;=\u0026thinsp;4.821, p\u0026thinsp;=\u0026thinsp;0.04, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eI) and S-ket (F (1, 22)\u0026thinsp;=\u0026thinsp;22.76, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eL) with no effect of genotype or interaction with genotype.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTask-induced change in low gamma power increased during maintenance when mice chose the correct response\u003c/h2\u003e \u003cp\u003eWe next sought to determine if task-induced change in gamma or low gamma differed across three distinct phases of the task (encoding, maintenance and retrieval, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) according to whether the mouse chose the correct or incorrect response in the choice phase. Spectral power heat maps were generated and we observed an induced low gamma signal specifically during the maintenance phase and specifically within the hippocampus, which appeared to be higher when mice made the correct choice compared to incorrect choice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). We next assessed and statistically compared change in low gamma power within the hippocampus in all mice (irrespective of treatment or genotype) across the 2 s of encoding, maintenance and retrieval and according to whether they selected the correct or incorrect choice. Change in low gamma power significantly increased across the 2 s maintenance period (main effect of time, F (2.797, 727.3)\u0026thinsp;=\u0026thinsp;10.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and was significantly increased during the maintenance period if the mouse chose the correct response compared to incorrect response (main effect of response, F (1, 260)\u0026thinsp;=\u0026thinsp;5.148, p\u0026thinsp;=\u0026thinsp;0.024, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) but low gamma was not altered by time or response choice during encoding, or retrieval (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cb\u003e\u0026amp;E\u003c/b\u003e). Change in low gamma was unaltered by correct or incorrect response during all three phases of encoding, maintenance and retrieval in the PFC (data not shown).\u003c/p\u003e \u003cp\u003eWe next sought to determine if genotype or drug influence change in hippocampal low gamma during the maintenance phase given change in low gamma was increased when mice made the correct compared to incorrect selection. Here, the later 1.5-2 s of the maintenance phase was assessed as this is the time point whereby the largest difference between correct and incorrect response can be found (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). In WT mice, MK-801 significantly reduced low gamma power during the later maintenance phase, irrespective of whether the mouse chose the correct or incorrect response (main effect of drug, F (1, 44)\u0026thinsp;=\u0026thinsp;4.122, p\u0026thinsp;=\u0026thinsp;0.04) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). No other drugs had a significant effect on low gamma power during this phase. In KO mice, there was no significant effect of any drugs on low gamma power during the maintenance phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study expectedly found that NMDAR antagonists consistently impair working memory; however, the effect of PCP was only present in WT, but not GluN2D KO mice, suggesting this receptor subunit is required for the action of PCP in WM. Furthermore, all drugs increased baseline gamma and/or low gamma power in the hippocampus and PFC to varying degrees, however, once again, for PCP this was genotype specific. In the hippocampus, PCP increased gamma power only in WT mice, not KO mice. In contrast, the effects of PCP on gamma power in PFC were exacerbated in KO mice. Finally, we identified upregulation of task-induced low gamma oscillations during the maintenance phase of the TUNL task when mice chose the correct response in the task. Finally, MK-801 significantly reduced low gamma activity during this maintenance period in WT but not KO mice.\u003c/p\u003e \u003cp\u003eWe report no differences between the genotypes in TUNL task accuracy. A recent study assessing conditional KO of the GluN2D subunit from PV interneurons similarly reported no impairments in PV-GluN2D-KO mice during the Y-maze task of spatial WM when compared with WT mice \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. However, they found impairments in short-term but not long-term memory during the Novel Object Recognition task and significant deficits in cognitive flexibility during the water T-maze test \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. These cognitive measures were not assessed in our study. However, it suggests that while deletion of the GluN2D subunit is not sufficient to cause WM impairments, the GluN2D subunit may be important for other cognitive functions such as object recognition memory and cognitive flexibility. In this study, we show diverging effects of the NMDAR antagonists PCP, MK-801, R-ket and S-ket on TUNL performance. Treatment with PCP leads to a decrease in overall accuracy in WT but not GluN2D-KO mice, suggesting that the WM-impairing effects of PCP are mediated by the GluN2D subunit. This differential drug response may be explained by the relative affinity of each drug for specific NMDAR subunits. For example, previous reports suggest that PCP is least potent for GluN2A but shows comparable potency for GluN2B-D subunits, while MK-801 is 10-fold more potent for GluN2A or B containing receptors \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, and ketamine has a higher potency at GluN1/2C as well as GluN1/2B subunit containing receptors \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Previous reports suggest that PV interneurons are particularly affected by PCP treatment, with prenatal PCP treatment selectively reducing PV density in the PFC and hippocampus, and these changes are linked with schizophrenia-like behavioural deficits including WM impairment \u003csup\u003e\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Our results suggest that PCP in disrupting WM primarily acts through GluN2D-containing NMDARs, which are mainly expressed on PV interneurons, altering the E/I balance in the brain\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. In contrast, MK-801 impaired overall accuracy in both genotypes which suggests that WM deficits following MK-801 are not mediated by the GluN2D subunit. A previous study showed that MK-801 dose-dependently impaired TUNL accuracy in both WT mice and mice lacking the obligatory GluN1 subunit of NMDARs from PV interneurons and forebrain pyramidal cells\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. This suggests that NMDAR hypofunction on interneurons may not be the primary mechanism underlying the WM deficits following MK-801 treatment. Alternatively, studies have linked the cholinergic and dopaminergic systems as well as changes in glial cells like astrocytes to the WM-inducing effects of MK-801 \u003csup\u003e64\u0026ndash;66\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eR-ket and S-ket did not impact accuracy in either genotype when considering effects over the entire session, however when examining accuracy over the first 12 trials alone, a significant reduction in accuracy was observed. This may reflect the shorter half-life of ketamine when compared with PCP or MK-801. The elimination half-life of ketamine is thought to be approximately 13 min in mice when administered i.p. compared with 46 min for PCP, whilst MK-801 has been shown to persist for 3 h following a single administration \u003csup\u003e\u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAll NMDAR antagonists increased baseline gamma power in the prefrontal cortex and to varying degress in the hippocampus. This aligns with the increase in baseline gamma power reported in people with schizophrenia \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e and in previous animal model studies showing acute treatment with NMDAR antagonists increases gamma power in both the hippocampus and frontal cortex \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. We extend this work to show novel findings that PCP only increases baseline gamma power in the hippocampus of WT mice, and GluN2D-KO mice are protected from this increase in baseline gamma power. Furthermore, the effect of PCP was also influenced by genotype in the PFC, but in an opposite manner, whereby GluN2D-KO mice show a heightened baseline gamma power increase in response to PCP. These genotype specific effects of PCP on gamma power may be an underlying mechanism by which GluN2D- KO mice are protected from the WM deficits induced by PCP. Previously, Sapkota et al., reported that ketamine induced a much larger increase in high gamma power in WT compared with GluN2D-KO mice and suggested that the GluN2D subunit may be critical for ketamine\u0026rsquo;s effect on neural oscillations \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Our data showed a main effect of genotype on low gamma power in the hippocampus for the R-ket treated group whereby WT mice showed a larger increase in gamma power in response to both saline and R-ket than GluN2D KO, however, within the PFC GluN2D-KO mice seemed to show a heightened response in both R-ket and S-ket groups. A major point of difference here is that we used depth electrodes and show striking regional differences, while Sapkota et al. used electrocorticographic analysis. Our data indicate differential effects of PCP depending both on genotype and region, and show that hippocampal changes in baseline gamma power align with working memory performance.\u003c/p\u003e \u003cp\u003eTask-induced changes in gamma power were evident during the maintenance phase of the task and specifically within the low gamma frequency range (30-40Hz). Low gamma during maintenance was significantly higher when mice went on to make the correct response in the choice phase, suggesting this is an electrophysiological correlate of working memory maintenance. In addition, this was significantly dampened by MK-801. This aligns with previous reports that people with schizophrenia show reduced task-induced gamma power specifically during the maintenance phase of WM \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. However, the study by Haenshel et al. also reported reduced theta and gamma activity during the retrieval phase in people with schizophrenia\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e and we did not find any significant effect of NMDAR antagonists on induced gamma power during retrieval. This highlights an important difference between drug-induced models and people with schizophrenia. A previous study in mice found that MK-801 disrupts theta-gamma co-modulation during the Y-maze spontaneous alternation task \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e, however, to the best of our knowledge, the effects of MK-801 on gamma or theta power during specific phases of WM in mice have not been previously reported. Given MK-801 showed the strongest behavioural effect on WM accuracy, it is not suprising that this was the only drug to cause a significant reduction in task-induced low gamma power.\u003c/p\u003e \u003cp\u003eIn conclusion, we report here that NMDAR antagonists disrupt WM accuracy, and characterise electrophysiological changes in the hippocampus and PFC which accompany these behavioural consequences of these drugs. We also add that PCP acts through the GluN2D subunit to exert its effects on baseline hippocampal gamma power and WM accuracy. Furthermore, we report here that the TUNL WM task induced a low gamma signal during the maintenance phase of the task and this signal was increased when mice went on to make the correct response in the choice phase, suggesting it is an important parameter in the ability of the mouse to complete the task correctly. MK-801 disrupted low gamma during the maintenance phase of the task, and showed the strongest effect in disrupting WM, again suggesting a critical link between low gamma during the maintenance phase and WM outcomes. Given the strong link between NMDAR hypofunction and schizophrenia and the burgeoning literature surrounding the specific role of the GluN2D subunit in schizophrenia \u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e, these data provide new and important insights into the molecular biology that may underlie working memory disruptions in schizophrenia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the International Society of Neurochemistry for providing financial support for this study as well as the National Health and Medical Research Council of Australia, GNT2000893 for Rachel Hill.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interst.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are open to sharing data upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCV\u003c/strong\u003e performed all live animal experiments, and performed data analysis and wrote the manuscript. \u003cstrong\u003eMH\u003c/strong\u003e assisted in surgical procedures, data collection and analysis and manuscript editing. \u003cstrong\u003eKI\u0026nbsp;\u003c/strong\u003eprovided the GluN2D-KO mice and contributed to manuscript editing. \u003cstrong\u003eSI\u003c/strong\u003e provided the GluN2D-KO mice and contributed to manuscript editing. \u003cstrong\u003eMM\u003c/strong\u003e provided the GluN2D-KO mice and contributed to manuscript editing. \u003cstrong\u003eSS\u003c/strong\u003e contributed to concerptualisation and manuscript editing. \u003cstrong\u003eNCJ\u0026nbsp;\u003c/strong\u003econtributed to conceptualisation, study design, data collection, data analysis and manuscript editing.\u0026nbsp;\u003cstrong\u003eRAH\u003c/strong\u003e conceptualised the study, assisted in study design, obtained funding for the study, assisted with data analysis and assisted in manuscript writing and editing.\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcGrath J, Saha S, Chant D, Welham J. 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International journal of molecular sciences 2023; 24(14).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5412811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5412811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWorking memory relies on synchronised network oscillations involving complex interplay between pyramidal cells and GABAergic interneurons. NMDA receptor (NMDAR) antagonists influence both network oscillations and working memory, but the relationship between these two consequences has not been elucidated. This study aimed to determine the effect of NMDAR antagonists on network oscillations during a working memory task in mice, and the contribution of the GluN2D receptor subunit.\u003c/p\u003e\n\u003cp\u003eAfter training wildtype (WT) and GluN2D-knockout (KO) mice on the Trial-Unique-Non-match to Location (TUNL) touchscreen task of working memory, recording electrodes were implanted into the prefrontal cortex (PFC) and hippocampus. Mice were challenged with either (S)-ketamine (30mg/kg), (R)-ketamine (30mg/kg), phencyclidine (PCP, 1mg/kg), MK-801 (0.3mg/kg) or saline prior to TUNL testing while simultaneous local field potential recordings were acquired.\u003c/p\u003e\n\u003cp\u003ePCP disrupted working memory accuracy in WT (p=0.001) but not GluN2D-KO mice (p=0.79). MK-801 (p\u0026lt;0.0001), (S)-ketamine (p\u0026lt;0.0001) and (R)-ketamine (p=0.007) disrupted working memory accuracy in both genotypes. PCP increased baseline gamma (30-80Hz) power in the hippocampus in WT (p=0.0015) but not GluN2D-KO mice (p=0.92). All drugs increased baseline gamma power in the PFC in both genotypes (p\u0026lt;0.05). Low gamma was induced during the maintenance phase of the TUNL task and increased when mice correctly completed the task (p=0.024). MK-801 disrupted task-induced low gamma in both genotypes (p=0.04).\u003c/p\u003e\n\u003cp\u003eIn summary, PCP action involves the GluN2D subunit of the NMDA receptor in the hippocampus to alter baseline gamma power and working memory. Task-induced low gamma activity during maintenance aligns with task performance, and is disrupted specifically by MK-801.\u003c/p\u003e","manuscriptTitle":"Differential effects of NMDAR antagonists on working memory and gamma oscillations, and the mediating role of the GluN2D subunit","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 06:28:32","doi":"10.21203/rs.3.rs-5412811/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be84867d-aabe-42db-92f9-aeb1f5cc6f61","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40003062,"name":"Biological sciences/Neuroscience"},{"id":40003063,"name":"Health sciences/Diseases/Psychiatric disorders/Schizophrenia"}],"tags":[],"updatedAt":"2024-12-19T14:02:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-18 06:28:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5412811","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5412811","identity":"rs-5412811","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}