Decreased overnight slow wave slope
change in anti-NMDAR encephalitis
and schizophrenia
Master Brain and Cognitive Sciences
Research Project 2
Student name:
Kanthida van Welzen
Student number:
10797424
Supervisor:
Albert Compte
UvA examiner:
Winni Hofman
Abstract
Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a disorder in which patients’ antibodies to the NMDAR result in psychiatric and cognitive symptoms similar to schizophrenia. Symptoms arise from NMDAR hypofunction and as anti-NMDAR encephalitis patients recover; symptoms attenuate. NMDARs are crucial for synaptic plasticity and two hypotheses have been proposed about synaptic plasticity in sleep: 1) repeatedly re-activation during slow wave sleep (SWS) is required for synaptic changes in order to encode memories and 2) during SWS global downscaling of synaptic strength is necessary to balance for the synaptic strengthening during wakefulness. SWS plays a role in both hypotheses, with slow wave slope being a marker for synaptic strength. In this study, we examined NMDAR hypofunction in anti-NMDAR encephalitis and schizophrenia by recording sleep EEG overnight and analysing the overnight slow wave slope change. In general, the slow wave slope decreased overnight, although younger participants showed an early night potentiation. After controlling for age, anti-NMDAR encephalitis participants and schizophrenia participants had a lower overnight slope change than controls. In patients with anti-NMDAR encephalitis this was restored as they recovered from the disease. Taken together, these results indicate that NMDAR hypofunction leads to a smaller overnight slope change as less global downscaling is needed to balance for the synaptic strengthening during wakefulness. Furthermore, these results are in favour of the second hypothesis and show that slow wave slope can be used as a marker for synaptic strength in diseases.
Keywords: anti-NMDAR encephalitis, schizophrenia, NMDAR, NMDAR hypofunction, synaptic plasticity, slow wave sleep
Decreased overnight slow wave slope
change in anti-NMDAR encephalitis
and schizophrenia
First described by Dalmau et al. in 2007, our understanding of anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis has greatly increased. Antibodies to the NMDAR decrease the surface density and synaptic localization of NMDARs (Hughes et al., 2008; Mikasova et al., 2012), resulting in a complex neuropsychiatric syndrome. At onset, patients have psychiatric symptoms (e.g. hallucinations, psychosis) and cognitive symptoms (e.g. impaired working memory) comparable to schizophrenia (Steiner et al., 2013; Al-Diwani et al., 2019). The similarity in psychiatric and cognitive symptoms between anti-NMDAR encephalitis and schizophrenia suggest that they arise from NMDAR hypofunction (Olney et al., 1999; Nakazawa and Sapkota, 2019). Although the underlying mechanisms for NMDAR hypofunction in schizophrenia are not clear, in anti-NMDAR encephalitis the loss of NMDARs is reversible with successful treatment. Symptoms attenuate, but cognitive deficits and sleep abnormalities remain (Dalmau et al., 2019).
Hypofunction of NMDAR has a great impact on cognition, because NMDARs are crucial in synaptic plasticity (Cull-Candy et al., 2001; Waxman and Lynch, 2005). Mediated by long-term depression and long-term potentiation (Yashiro and Philpot, 2008; Volianskis et al., 2013), these serve as the cellular substrates for learning and memory (Morris, 2006; 2013). During learning, events will be encoded in the hippocampus and neocortical networks. A period of sleep after learning is necessary to consolidate memories (Diekelmann and Born, 2010). Two alternative hypotheses of synaptic plasticity in sleep have been proposed and they are not mutually exclusive.
The active system consolidation hypothesis proposes that memories are actively consolidated by repeatedly re-activating the encoded memories of the day during slow wave sleep (SWS) (Stickgold, 2005). Synchronization of the SWS, sharp wave-ripples and spindles lead to persisting synaptic plastic changes through the NMDAR (Marshall and Born, 2007).
The second hypothesis, the synaptic homeostasis hypothesis assumes that wakefulness leads to a net increase in synaptic strength (Tononi and Cirelli, 2003; 2006; 2014). Subsequent sleep would globally downscale synaptic strength to a level that is sustainable and allows for re-use of synapses for the next wakeful period. SWS is associated with this downscaling: at the start of sleep, slow wave amplitudes are maximal and by the end of a sleep period they have decreased in a similar manner as synaptic strength. In this case, memory consolidation is a by-product, assuming that the downscaling is proportional in all synapses, nullifying weak synapses whereas strong potentiated synapses will have improved signal-to-noise ratio.
SWS plays a role in both hypotheses and represents the sleep pressure in a homeostatic manner: sleep slow wave activity (SWA, EEG power 0.5-4.0 Hz) increases as a function of prior wakefulness and decreases as sleep progresses (Borbély, 1982). Studies have explored the underlying mechanisms of sleep SWA and found that SWA reflects the strength of cortical synapses. With a large-scale sleeping thalamo-cortical system model, the simulations showed that reduced synaptic strength produced a decrease in sleep SWA including changes in several slow-wave parameters, such as decreased amplitude and
shallower slopes (Esser et al., 2007). In two companion papers, the model predictions were tested in rats and in humans (Vyazovskiy et al., 2007; Riedner et al., 2007). Both rat LFP recordings and human EEG recordings showed a decrease in sleep SWA between early and late sleep, accompanied by a decrease in amplitude and a reduced slope of the slow waves. These papers support the notion that the slope of the slow waves represents a possible reliable marker of cortical synaptic strength.
As NMDARs are necessary for synaptic strength, we hypothesize that NMDAR hypofunction in anti-NMDAR encephalitis and schizophrenia would alter synaptic plasticity and this would be reflected in the slope of slow waves. We recorded sleep EEG overnight in anti-NMDAR encephalitis patients and schizophrenia patients and analysed the evolution of slope of slow waves overnight. Anti-NMDAR encephalitis patients were followed up by 3, 6, and 12 months to determine whether sleep alterations reversed with recovery.
METHODS
Participants
We included 22 healthy control participants (mean age 24.6±9.4 years, range 14.2-55.5, 18 females), 25 anti-NMDAR encephalitis patients (mean age 28.8±11.6 years, range 10.4-57.1, 21 females), and 20 schizophrenia patients (mean age 23.2±8.7 years, range 14.9-49.5, 11 females). Psychiatric diagnoses (or the absence thereof for controls) were confirmed using the Structured Clinical Interview for DSM IV (First et al., 1996). Anti-NMDAR encephalitis patients were recruited at the moment of hospital discharge from different centers (in Spain, Germany and United Kingdom) and completed the experiment around 4.3 months after disease onset (median, IQR 2.4-6.5 months). All patients fulfilled clinical diagnostic criteria of anti-NMDAR encephalitis with confirmation of CSF IgG antibodies against the GluN1 subunit of the NMDAR (Graus et al., 2016). Schizophrenia patients were recruited from Hospital Clínic (Barcelona, Spain). They were tested 49.5 months after diagnosis (median, IQR 14.7-102.0 months) and were clinically stable at the time of testing. Controls were age- and sex-matched and were recruited from Barcelona and surroundings. All participants (and, in the case of minors of age, their legal guardians) provided written informed consent and were monetarily compensated for their time and travel expenses. It has been reviewed and approved by the Research Ethics Committee of Hospital Clínic.
Design
Participants took part in a one-year longitudinal study. Anti-NMDAR encephalitis patients were tested at 3, 6, and 12 months after recruitment again, four sessions in total (will be referred to as session 1 to 4). Controls and schizophrenia patients were tested in two sessions, 12 months interval (referred to as session 4). Participants underwent cognitive tasks, MRI, EEG and video-polysomnography (V-PSG; sleep EEG). Since we are only interested in sleep, only the V-PSG will be assessed in this study. V-PSG was performed using a digital polygraph system Deltamed (Paris, France) from Jan-2018 to Feb-2019, and BrainRT™ (Waarloos, Belgium) from Feb-2019 on) with the Braintronics B.V. Inbox-1166 A in both cases. Sampled at 512 Hz, a 43-channel EEG was recorded with electrodes placed according to the 10/10 system (Fp1, Fpz, Fp2, AF7, AFz, AF8, F7, F3, Fz, F4, F8, FT7, FC3, FCz FC4, FT8, A1, T7, C5, C3, Cz, C4, C6, T8, A2, TP7, CP3, CPz, CP4, TP8, P7, P3, Pz, P4, P8, PO7, PO3, POz, PO4, PO8, O1, Oz, O2). The PSG was visually scored by an expert for sleep stages (30s epochs) and inspected for arousals and artifacts according to the AASM criteria (Barry et al., 2018).
EEG pre-processing and wave detection
After low-pass filtering at 30 Hz, the signal was re-referenced to the average of the two earlobes using the MNE package in Python (Gramfort et al., 2013). Slow wave detection was performed with Yet Another Spindle Algorithm (YASA), a sleep analysis toolbox in Python (Vallet & Jajcay, 2020; Figure 1). Firstly, YASA bandpass filters the signal between 0.3-2.0 Hz. Since slow waves occur in sleep stages N2 and N3, it detects slow waves only in these sleep stages by taking into account the hypnogram. For each wave, several parameters are calculated, such as the slow wave slope, our marker of interest. The slope is defined as the amplitude of the most negative peak divided by the time until the next zero crossing.
Data analysis
We decided to split the data into two analyses because of a lack of schizophrenia patients in session 4 (the one-year follow-up). The first analysis compared the overnight slow wave slope change for the three groups in session 1 and the second analysis compared the slow wave slopes over sessions for controls and anti-NMDAR encephalitis.
Firstly, as we were going to analyse the data of the entire night for each subject, we had to take into consideration that subject’s sleep times vary. Therefore, their sleep time was rescaled from 0 to 1 instead of working with the original time.
Subsequently, we had to take into account that slow waves are affected by many factors and the most influencing factors were considered: sleep stage, position on scalp, age, and recording system. Only slow waves of sleep stage N3 were analysed, as slow waves are the prominent feature in this sleep stage whereas in N2 slow waves occur sporadically. As for
Figure 1. Example of EEG signal
Top trace: representative 20-s EEG trace from the Fp1 channel for a control subject; bottom trace:
corresponding band-pass filtered signal (0.3-2.0 Hz) with wave detections highlighted in blue, as performed by YASA. The blue bold lines represent the calculated slopes. The definition of the slope is the amplitude of the most negative peak divided by the time until the next zero crossing.
position on the scalp, only frontal electrodes (Fp1, Fpz, Fp2, AF7, AFz, AF8, F7, F3, Fz, F4, F8) were considered because slow waves originate more frequently in prefrontal-orbitofrontal regions and propagate in an anteroposterior direction (Massimini et al., 2004). Moreover, age seemed to affect the slope the most. Previous research has found that the overnight slope change occurred across all ages, but the overnight slope change was largest in children and decreased towards early adulthood (Kurth et al., 2010; Jaramillo et al., 2020). In order to control for age, we used a spline model on data of controls with slope as dependent variable and time, age and the interaction as predictors. This age-controlled model was used to predict the overnight slope change for anti-NMDAR encephalitis and schizophrenia. The difference between the predicted and the actual slope change, the residuals, were calculated and used for both analyses. In the first analysis, the residuals for anti-NMDAR encephalitis and schizophrenia were evaluated whether they differ from the controls. For the second analysis, a linear mixed-effects model was used to compare the overnight slope change over sessions for controls and anti-NMDAR encephalitis patients. The residuals were the dependent variable and time, group, session and the interaction between time and recording system as fixed factors. Subjects were taken into account as random intercepts and time as random slope. In the second analysis we had to consider the recording system because midway the study the recording system was changed. It was found that this affected the amplitude of the slow waves recorded.
Results
Subjects underwent V-PSG the entire night similar to a typical night with normal human sleep and on average people slept well, felt rested and awake (table 1). There were no significant differences between groups within sessions, except that schizophrenia patients had more N3 sleep than controls in session 4 (p<0.05). As for the subjective measures, schizophrenia patients reported to have slept better than controls in session 1 (p<0.05) and anti-NMDAR encephalitis patients felt significantly less rested than schizophrenia patients in session 4 (p<0.05).
Table 1. Basic sleep measures per session and per group
Data for objective and subjective measures shown as mean ± standard deviation. TST refers to total sleep time; Sleep QL as sleep quality defined as the percentage of REM and N3 sleep of the total sleep time; Sleep EF is sleep efficiency defined as the percentage of total sleep time over time in bed. For the subjective measures a questionnaire was filled in with multiple choice options for the second to fourth question. For the second question: 0 = very bad; 1 = bad, 2 = regularly, 3 = good, 4 = very good. For the third and fourth questions: 0 = worse than last night, 1 = nothing, 2 = something, 3 = a lot. *, p<0.05.
Age influences slope in early night
To control for age, we decided on a spline model with knots at t=0.25, 0.60, 0.80 (AIC = 6709027) because it outperforms linear (AIC = 6714898) and polynomial models (AIC = 6709046). Only mixed linear models outperformed the spline model, with a lower AIC than 6675505, but the linear fit did not resemble the data. The age model revealed that the slope decreased overnight for all ages (figure 2A). However, the slope change overnight was highly dependent on age. Younger participants had an increase in slope at the beginning of the night (t=0.0-0.25) and this slope potentiation became smaller with age until around 30 years old it was absent (figure 2B). This slope potentiation had an exponential fit with age.
A B
C D E
Figure 2. Influence of age on overnight slope change
A. Dashed lines; the overnight slope ± SEM for control subjects split into quartiles. Every quartile contains five
control subjects, shown is their mean age. Solid lines; the overnight slope as predicted by the age-controlled spline model. The age-controlled spline model took into account time, age and their interaction with knots at t=0.25, 0.60, 0.80. B. The slope difference in early night (t =0.00-0.25) per age with exponential fit. C-D. Overnight slope ± SEM in session 1 and 4, one year after, for young (C) and old control subjects (D). Twelve control subjects underwent both sessions and were split up by the median into a young and old group. E. The slope difference between session 1 and 4 for young and old control subjects. The yellow and green solid squares in the upper part of the figure mark significant differences between session 1 and 4 for young and old participants, respectively (one-sided paired permutation test; p<0.05, n=6 participants). The black solid squares mark significant differences between young and old participants (one-sided permutation test, p<0.05, n = 12 participants).
To explore the early night slope potentiation in more detail, we did a paired permutation test separately for young and old participants by splitting by the median, resulting in six control participants in each group. First, a paired permutation test was performed to evaluate whether the overnight slope change is decreased in session 4 than in session 1 (figure 2C-E). For young participants, the slope was significantly steeper in session 1 than in session 4 around t=0.2-0.4 and t=0.75-0.85 (p<0.05). In older participants, only at t=0.6 session 1 had a higher slope than session 4 (p<0.05). Second, to evaluate whether age and session interact, a paired difference between session 1 and 4 was calculated and compared for young and old participants in a permutation test. Mainly around t=0.2-0.4 showed a significant steeper slope for young participants over old participants (p<0.05).
Anti-NMDAR encephalitis and schizophrenia patients have a lower
overnight slope
We predicted overnight slope change for anti-NMDAR encephalitis and schizophrenia patients with the age spline model on control subjects. The residuals, the difference between the actual and the predicted slopes, were compared. It was hypothesized that if NMDAR hypofunction caused a shallower slope for anti-NMDAR encephalitis and schizophrenia, the residuals would be negative, because the age spline model would overestimate their slope. It was found that anti-NMDAR encephalitis and schizophrenia had mostly negative residuals from t=0.2 to t=0.85 (figure 3). This indicates that anti-NMDAR encephalitis and schizophrenia patients had a lower overnight slope change than controls at the same age.
Anti-NMDAR encephalitis’ overnight slope change recovers with
time
Here again, the residuals were taken for controls and anti-NMDAR encephalitis for sessions 1 to 4 and analysed with a linear mixed-effects model (table 2). After controlling for recording
Figure 3. Residuals for anti-NMDAR encephalitis and schizophrenia
Residuals, the difference between the actual slope and predicted slope by the spline model overnight, for anti-NMDAR encephalitis and schizophrenia patients in session 1. 95% confidence intervals are bootstrapped. Encephalitis = anti-NMDAR encephalitis.
Predictor Coef. β SE (β) z p
Intercept 31.02 24.60 1.26 0.2 Group (Enc) -111.61 33.94 -3.29 <0.01 Recording system (Old) -21.60 2.30 -9.41 <0.001 Rescaled time 31.63 14.90 2.12 <0.05 Rescaled time × Group (Enc) 10.26 19.73 0.52 0.6 Rescaled time × Recording system (Old) -8.38 4.46 -1.88 0.06 Session -17.29 0.83 -20.80 <0.001 Group (Enc) × Session 36.38 0.95 38.12 <0.001 Rescaled time × Session -8.26 1.59 -5.20 <0.001 Rescaled time × Session × Group (Enc) -10.9 1.85 -5.91 <0.001
Table 2. Result summary of linear mixed-effects model for controls and anti-NMDAR encephalitis over sessions 1 to 4
Summary of all the predictors in the analysis: coefficient estimates
β, standard errors SE(β), associated Wald’s z-score
system (β = -21.6, SE = 2.3, p < 0.01), the main effect of group was significant (β = -111.6, SE = 33.9, p < 0.01), showing that anti-NMDAR encephalitis had a shallower slope than controls. The main effect of time was also significant (β = 31.6, SE = 14.9, p < 0.05); as the night progressed, residuals increased. However, the interaction effect between group and time was insignificant (p = 0.6). Unexpectedly, the main effect of session was significant (β = -17.3, SE = 0.8, p < 0.01), revealing that in controls session 4 had a lower overnight slope than session 1. Crucially, the interaction effect between group and session was significant (β = 36.4, SE = 1.0, p < 0.01). With every session, the slope increased in anti-NMDAR encephalitis. The interaction effect between time and session (β = -8.3, SE = 1.6, p < 0.01) and the two-way interaction effect (β = -10.9, SE = 1.8, p < 0.01) between time, session, and group were significant; with every next session, the slope decreased over time of the night, with a stronger decrease for anti-NMDAR encephalitis patients than controls (figure 4).
Discussion
The current study used overnight EEG to examine slow wave slope changes in anti-NMDAR encephalitis patients and schizophrenia patients as a consequence of NMDAR hypofunction. Furthermore, it was questioned whether slow wave slope changes would be reversed in anti-NMDAR encephalitis patients with recovery. We found, after controlling for age, that in healthy controls the slow wave slope decreased as night progressed. In anti-NMDAR encephalitis patients and schizophrenia patients, this overnight slope decreased even more. However, as anti-NMDAR encephalitis patients recover from the disease, their overnight slope change increased.
Slow wave slope as a reliable marker for synaptic strength
Slow wave slope has been used as a direct measure of synaptic strength and it might be questioned how reliable this is. Undoubtedly, the slope of slow waves is not only determined by synaptic strength as neuromodulators, metabolic factors and synchronisation of neurons are likely to affect slow wave slope too (Riedner et al., 2007). These influences can affect both the amplitude and the period of the slow wave, resulting in different slopes. However, in previous modeling work it has been shown directly that changing the synaptic strength in sleep
A B C
Figure 4. Residuals slope for controls and anti-NMDAR encephalitis over sessions
A, C. Dashed lines; residuals, the difference between the actual and predicted slopes overnight, for session 1
and session 4 in control subjects (A) and anti-NMDAR encephalitis (C) with the bootstrapped 95% confidence intervals. B. Dashed lines; the residuals for session 1 to 4 for anti-NMDAR encephalitis. In all figures, the solid lines represent the residuals overnight as fitted by the linear mixed-effects model.
SWA is sufficient to change slow wave slope (Esser et al., 2007). This has been further confirmed with LFP and EEG recordings in rats and humans, respectively (Vyazovskiy et al., 2007; Riedner et al., 2007). Moreover, slow waves occur through synchronisation of neurons, transitioning between the up state and down state (Tononi and Cirelli, 2006; Daan et al., 1984; Timofeev et al., 2000). The rate of synchronisation is directly affected by synaptic strength, because the stronger the synapses, the more synaptic activity and stronger connections between neurons, leading to more synchronisation in a population of neurons. Therefore, the slope of slow waves may be the most reliable representative of synaptic strength in the EEG signal, because EEG signals are a result of synchronisation of activity in a population of neurons and the rate of synchronisation determines the slow wave slope.
Traditionally, slope of evoked waves has been used as an electrophysiologic marker of synaptic strength and mainly in studies of long-term potentiation and depression (Glazewski et al., 1998; O’Boyle et al., 2004; Whitlock et al., 2006). Although slow waves in SWS are not evoked by stimulation, to some extent they are evoked by spontaneous activity in the cortex, as slow waves usually originate in the frontal cortex (Massimini et al, 2004). Thus, as reliable as the slope of evoked waves is a marker for synaptic strength, so is the slope of slow waves.
Influence of age on the slow wave slope
Although the intention of this research was not to examine the age-dependency on slow wave slope, we found an interesting result. Earlier research demonstrated by comparing the first and last hour or episode of NREM sleep that the slope became shallower as sleep progressed, but this slope change was greatest in children and decreased in adolescents (Kurth et al., 2010; Jaramillo et al., 2020). We analysed the data over the entire night and we did not find that overnight slope change is dependent on age. However, slope decreased in the same amount for all ages from start to the end of the night. Slope became steeper in the early night the younger the participant was, followed by the decrease in slope as sleep continued.
These results support both hypotheses of synaptic plasticity in SWS. The overnight slope decrease found in all ages supports the synaptic homeostasis hypothesis which proposes that SWS is necessary for synapse renormalization by down-scaling synaptic strength (Tononi and Cirelli, 2003; 2006). However, the early night potentiation found in younger participants confirms to the active system consolidation hypothesis, suggesting that repeatedly re-activation is crucial to encode memories (Stickgold, 2005). These hypotheses are not mutually exclusive and therefore it could be that the slow wave slope represent both mechanisms suggested by these hypotheses.
However, the early night potentiation is only found in children and young adults (<28 years old), whereas the active system consolidation would occur across all ages. A more plausible explanation for this age-dependency in early night potentiation might be development. During development, plastic changes are the largest, reflected in both structural as functional changes. As an example, synapse density increased until adolescence, and decreased afterwards (Huttenlocher and Dabholkar, 1997). In our study, an exponential relationship was found between the early night potentiation and age; early night potentiation became smaller with age until ~28 years old, when older only a decrease in slope was seen. It indicates that development of the brain might influence the slow wave slope in early night. Why it only affects early night should still be researched.
Overnight slow wave slope decreased in anti-NMDAR encephalitis
and schizophrenia
As expected, the overnight slope change was more decreased in anti-NMDAR encephalitis patients and schizophrenia patients. Furthermore, anti-NMDAR encephalitis’ overnight slope change increased with time as patients recovered from the disease. These results suggest that slow wave slope is shallower because of impaired synaptic plasticity mediated by NMDAR hypofunction in anti-NMDAR encephalitis and schizophrenia.
Considering the synaptic homeostasis hypothesis (Tononi and Cirelli, 2003; 2006), the shallower overnight slope change found in anti-NMDAR encephalitis and schizophrenia could be interpreted as less need for synapse renormalization during sleep. This implies that during wakefulness there is less increase in synaptic strengthening as a result of NMDAR hypofunction. With regard to our results, anti-NMDAR encephalitis and schizophrenia start the night with a shallower slope than controls (i.e. less synaptic strength), and therefore less downscaling (i.e. shallower slope overnight) is needed to reach the same level of synapse renormalization.
Slow wave slope represents synaptic plasticity, but not the function of NMDAR. Although NMDARs play an important role in synaptic strength, other factors such as coactivation with AMPARs and Ca2+ levels are necessary too. We cannot exclude these factors from affecting synaptic plasticity and therefore slow wave slope, but previous research demonstrated that NMDAR in SWS are required for synaptic plasticity. By knocking out subunits of the NMDAR, synaptic plasticity was suppressed (Liu et al., 2016). Similar results were found when blocking the NMDAR (Chauvette et al., 2012; Yang et al., 2014). Therefore, NMDAR is necessary for synaptic plasticity in SWS.
Though the underlying mechanisms underlying NMDAR hypofunction in schizophrenia are unclear, in anti-NMDAR encephalitis NMDAR hypofunction directly affects synaptic plasticity. Patients’ antibodies decrease the presence of NMDARs in the synapses and subsequently eliminating NMDAR-mediated synaptic plasticity (Mikasova et al., 2012). Furthermore, the magnitude of these effects depended on the number of antibodies and the effects were reversible (Hughes et al., 2010). This goes in line with the current results, showing that as antibodies decrease with time, slow wave slope change overnight increases in anti-NDMAR encephalitis. Thus, NMDAR hypofunction affects synaptic plasticity, resulting in changes in slow wave slope.
Our support for NMDAR hypofunction would have been strengthened if we had been able to analyse all participant groups over time. With a lack of schizophrenia patients for the fourth session, we were not able to examine whether their overnight slope change would have been similar to the first session. This was expected as these patients were clinically stable by their medication, implying that the schizophrenic symptoms were under control and so the amount of NMDAR hypofunction would not have altered. Moreover, it was unexpected that controls had an overall lower slope in session 4 than in session 1. We do not have an explanation for this discrepancy as the objective and subjective measures do not significantly differ between sessions for controls. Even though controls had a lower overnight slope, anti-NMDAR encephalitis patients still showed an increase in overnight slope.
Conclusion
In this study, slow wave slope was used as a marker for synaptic strength to examine how NMDAR hypofunction affects anti-NMDAR encephalitis and schizophrenia. We found that the
slow wave slope was decreased over the entire night in schizophrenia and anti-NMDAR encephalitis, with in the latter recovery as they got treatment. Besides, overnight slope change is in particularly affected by age, showing an early night potentiation in children and adolescents whereas it is abolished in adults. In general, overnight slow wave slope becomes shallower overnight regardless age, supporting the synaptic homeostasis hypothesis. The overnight slope change is smaller in anti-NMDAR encephalitis and schizophrenia, indicating that NMDAR hypofunction affects synaptic plasticity. Future studies are needed to assess the relationship between NMDAR hypofunction, synaptic plasticity, and slow wave slope on a neuronal level. It would strengthen the notion that slow wave slope is a reliable marker for synaptic strength. Furthermore, this study demonstrates that slow wave slope can be used to assess impairments in synaptic plasticity in schizophrenia and anti-NMDAR encephalitis. It would be of great interest to use slow wave slope in therapeutic interventions to restore healthy overnight slope change in these and maybe other diseases.
References
Al-Diwani, A., Handel, A., Townsend, L., Pollak, T., Leite, M. I., Harrison, P. J., ... & Irani, S. R. (2019). The psychopathology of NMDAR-antibody encephalitis in adults: a systematic review and
phenotypic analysis of individual patient data. The Lancet Psychiatry, 6(3), 235-246.
Barry RB, Albertario CL, Harding SM, et al.; for the American Academy of Sleep Medicine (2018). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. Version 2.5. Darien, IL: American Academy of Sleep Medicine.
Borbély, A. A. (1982). A two process model of sleep regulation. Hum neurobiol, 1(3), 195-204. Chauvette, S., Seigneur, J., & Timofeev, I. (2012). Sleep oscillations in the thalamocortical system induce long-term neuronal plasticity. Neuron, 75(6), 1105-1113.
Cull-Candy, S., Brickley, S., & Farrant, M. (2001). NMDA receptor subunits: diversity, development and disease. Current opinion in neurobiology, 11(3), 327-335.
Daan, S., Beersma, D. G., & Borbély, A. A. (1984). Timing of human sleep: recovery process gated by a circadian pacemaker. American Journal of Physiology-Regulatory, Integrative and
Comparative Physiology, 246(2), R161-R183.
Dalmau, J., Tüzün, E., Wu, H. Y., Masjuan, J., Rossi, J. E., Voloschin, A., ... & Mason, W. (2007). Paraneoplastic anti–N‐methyl‐D‐aspartate receptor encephalitis associated with ovarian teratoma. Annals of neurology, 61(1), 25-36.
Dalmau, J., Armangué, T., Planagumà, J., Radosevic, M., Mannara, F., Leypoldt, F., ... & Graus, F. (2019). An update on anti-NMDA receptor encephalitis for neurologists and psychiatrists: mechanisms and models. The Lancet Neurology, 18(11), 1045-1057.
Diekelmann, S., & Born, J. (2010). The memory function of sleep. Nature Reviews
Neuroscience, 11(2), 114-126.
Esser, S. K., Hill, S. L., & Tononi, G. (2007). Sleep homeostasis and cortical synchronization: I. Modeling the effects of synaptic strength on sleep slow waves. Sleep, 30(12), 1617-1630.
First, M. B., Spitzer, R. L., Gibbon, M. & Williams, J. B. W (1996). Structured Clinical Interview for DSM-IV Axis I Disorders, Clinician Version (SCID-CV). American Psychiatric Press, Inc.).
Glazewski, S., Herman, C., McKenna, M., Chapman, P. F., & Fox, K. (1998). Long-term potentiation in vivo in layers II/III of rat barrel cortex. Neuropharmacology, 37(4-5), 581-592.
Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., ... & Hämäläinen, M. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in neuroscience, 7, 267.
Graus, F., Titulaer, M. J., Balu, R., Benseler, S., Bien, C. G., Cellucci, T., ... & Glaser, C. A. (2016). A clinical approach to diagnosis of autoimmune encephalitis. The Lancet Neurology, 15(4), 391-404.
Hughes, E. G., Peng, X., Gleichman, A. J., Lai, M., Zhou, L., Tsou, R., ... & Balice-Gordon, R. J. (2010). Cellular and synaptic mechanisms of anti-NMDA receptor encephalitis. Journal of
Neuroscience, 30(17), 5866-5875.
Huttenlocher, P. R., & Dabholkar, A. S. (1997). Regional differences in synaptogenesis in human cerebral cortex. Journal of comparative Neurology, 387(2), 167-178.
Jaramillo, V., Volk, C., Maric, A., Furrer, M., Fattinger, S., Kurth, S., ... & Huber, R. (2020). Characterization of overnight slow-wave slope changes across development in an age-, amplitude-, and region-dependent manner. Sleep.
Kurth, S., Jenni, O. G., Riedner, B. A., Tononi, G., Carskadon, M. A., & Huber, R. (2010). Characteristics of sleep slow waves in children and adolescents. Sleep, 33(4), 475-480.
Liu, S., Liu, Q., Tabuchi, M., & Wu, M. N. (2016). Sleep drive is encoded by neural plastic changes in a dedicated circuit. Cell, 165(6), 1347-1360.
Marshall, L., & Born, J. (2007). The contribution of sleep to hippocampus-dependent memory consolidation. Trends in cognitive sciences, 11(10), 442-450.
Massimini, M., Huber, R., Ferrarelli, F., Hill, S., & Tononi, G. (2004). The sleep slow oscillation as a traveling wave. Journal of Neuroscience, 24(31), 6862-6870.
Mikasova, L., De Rossi, P., Bouchet, D., Georges, F., Rogemond, V., Didelot, A., ... & Groc, L. (2012). Disrupted surface cross-talk between NMDA and Ephrin-B2 receptors in anti-NMDA encephalitis. Brain, 135(5), 1606-1621.
Morris, R. G. M. (2006). Elements of a neurobiological theory of hippocampal function: the role of synaptic plasticity, synaptic tagging and schemas. European Journal of Neuroscience, 23(11), 2829-2846.
Morris, R. G. (2013). NMDA receptors and memory encoding. Neuropharmacology, 74, 32-40. Nakazawa, K., & Sapkota, K. (2020). The origin of NMDA receptor hypofunction in schizophrenia. Pharmacology & Therapeutics, 205, 107426.
O'Boyle, M. P., Do, V., Derrick, B. E., & Claiborne, B. J. (2004). In vivo recordings of long-term potentiation and long-term depression in the dentate gyrus of the neonatal rat. Journal of
neurophysiology, 91(2), 613-622.
Olney, J. W., Newcomer, J. W., & Farber, N. B. (1999). NMDA receptor hypofunction model of schizophrenia. Journal of psychiatric research, 33(6), 523-533.
Raphael Vallat and Nikola Jajcay (2020, May 9). raphaelvallat/yasa: v0.3.0 (Version v0.3.0). Zenodo. http://doi.org.10.5281/zenodo.3818479
Riedner, B. A., Vyazovskiy, V. V., Huber, R., Massimini, M., Esser, S., Murphy, M., & Tononi, G. (2007). Sleep homeostasis and cortical synchronization: III. A high-density EEG study of sleep slow waves in humans. Sleep, 30(12), 1643-1657.
Steiner, J., Walter, M., Glanz, W., Sarnyai, Z., Bernstein, H. G., Vielhaber, S., ... & Klingbeil, C. (2013). Increased prevalence of diverse N-methyl-D-aspartate glutamate receptor antibodies in patients with an initial diagnosis of schizophrenia: specific relevance of IgG NR1a antibodies for distinction from N-methyl-D-aspartate glutamate receptor encephalitis. JAMA psychiatry, 70(3), 271-278.
Stickgold, R. (2005). Sleep-dependent memory consolidation. Nature, 437(7063), 1272-1278. Timofeev, I., Grenier, F., Bazhenov, M., Sejnowski, T. J., & Steriade, M. (2000). Origin of slow cortical oscillations in deafferented cortical slabs. Cerebral cortex, 10(12), 1185-1199.
Tononi, G., & Cirelli, C. (2003). Sleep and synaptic homeostasis: a hypothesis. Brain research
bulletin, 62(2), 143-150.
Tononi, G., & Cirelli, C. (2006). Sleep function and synaptic homeostasis. Sleep medicine
reviews, 10(1), 49-62.
Tononi, G., & Cirelli, C. (2014). Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron, 81(1), 12-34.
Volianskis, A., Bannister, N., Collett, V. J., Irvine, M. W., Monaghan, D. T., Fitzjohn, S. M., ... & Collingridge, G. L. (2013). Different NMDA receptor subtypes mediate induction of long‐term potentiation and two forms of short‐term potentiation at CA1 synapses in rat hippocampus in vitro. The
Vyazovskiy, V. V., Riedner, B. A., Cirelli, C., & Tononi, G. (2007). Sleep homeostasis and cortical synchronization: II. A local field potential study of sleep slow waves in the rat. Sleep, 30(12), 1631-1642.
Waxman, E. A., & Lynch, D. R. (2005). N-methyl-D-aspartate receptor subtypes: multiple roles in excitotoxicity and neurological disease. The Neuroscientist, 11(1), 37-49.
Whitlock, J. R., Heynen, A. J., Shuler, M. G., & Bear, M. F. (2006). Learning induces long-term potentiation in the hippocampus. Science, 313(5790), 1093-1097.
Yang, G., Lai, C. S. W., Cichon, J., Ma, L., Li, W., & Gan, W. B. (2014). Sleep promotes branch-specific formation of dendritic spines after learning. Science, 344(6188), 1173-1178.
Yashiro, K., & Philpot, B. D. (2008). Regulation of NMDA receptor subunit expression and its implications for LTD, LTP, and metaplasticity. Neuropharmacology, 55(7), 1081-1094.
