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Impairment of neural coordination in hippocampal neuronal ensembles after a psychotomimetic dose of dizocilpine
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Impairment of neural coordination in hippocampal neuronal ensembles after a psychotomimetic dose of dizocilpine Ewa Szczurowskac,1, Nikhil Ahujaa,1, Přemysl Jiruškab, Eduard Kelemenc,⁎, Aleš Stuchlíka,⁎⁎ a b c
Department of Neurophysiology of Memory and Institute of Physiology, Czech Academy of Sciences, Vídeňská 1083, 142 20 Prague, Czech Republic Department of Developmental Epileptology, Institute of Physiology, Czech Academy of Sciences, Vídeňská 1083, 142 20 Prague, Czech Republic National Institute of Mental Health, Topolová 748, 250 67 Klecany, Czech Republic
A R T I C L E I N F O
A B S T R A C T
Keywords: Psychosis MK-801 Neuronal discoordination Hippocampus Theta rhythm
The discoordination hypothesis of schizophrenia posits discoordination of neural activity as the central mechanism that underlies some psychotic symptoms (including ‘hallmark’ cognitive symptoms) of schizophrenia. To test this proposition, we studied the activity of hippocampal neurons in urethane anesthetized Long Evans rats after 0.15 mg/kg dizocilpine (MK-801), an N-Methyl-D-aspartate (NMDA) glutamate receptor antagonist, which can cause psychotic symptoms in humans and cognitive control impairments in animals. We observed that MK-801 altered the temporal coordination, but not rate, of neuronal firing. Coactivation between neurons increased, driven primarily by increased coincident firing of cell pairs that did not originally fire together before MK-801 injection. Increased pairwise coactivation manifested as disorganized discharge on the level of neuronal ensembles, which in turn could lead to disorganization in information processing. Disorganization of neuronal activity after a psychotomimetic dose of MK-801 supports the discoordination hypothesis of psychosis.
1. Introduction According to the discoordination hypothesis of schizophrenia, discoordination of activity within neuronal networks is the neural mechanism that underlies symptoms of cognitive disorganization (Phillips and Silverstein, 2003). Central to this hypothesis is the concept of cell assemblies: coalitions of coactive cells that constitute the fundamental functional units of information encoding and processing in the brain (Hebb, 1949; Harris et al., 2003). The importance of coordination in neuronal firing for information processing is supported by a substantial body of evidence; for example, synchronization of neuronal activity in the visual cortex has been implicated in visual scene segmentation (Engel et al., 1991), and coordinated activation of cell assemblies in the hippocampus was shown to organize distinct, competing spatial representations (Kelemen and Fenton, 2010, 2016; Jezek et al., 2011). Organization of neuronal activity has been characterized on timescales ranging from tens of milliseconds to seconds (Olypher et al., 2002; Harris et al., 2003; Jezek et al., 2011; Kelemen and Fenton, 2010, 2013). The timing of synchronized neuronal firing within cell assemblies is organized by rhythmical gamma (Engel et al., 1991; Harris et al., 2003) and theta activity (O'Keefe and Recce, 1993; Dragoi and Buzsáki, 2006; Jezek et al., 2011).
Disturbances in the temporal organization of neuronal firing are the hypothesized cause of impaired information processing in mental disorders, including schizophrenia (Uhlhaas and Singer, 2006; Olypher et al., 2006; Jones, 2010; Moghaddam & Wood, 2014; Fenton, 2015). According to this hypothesis, psychosis is related to impairments in coordination of activity between neurons – a phenomenon that we call discoordination. Discoordination would occur for example, if neurons that normally discharge together stopped firing together, or if neurons that normally discharge separately started firing together, or if combination of the two effects occurred. In this theoretical framework, discoordination manifests on the cognitive level by incomplete, fuzzy or otherwise damaged mental representations. Discoordination in firing between different cell assemblies leads to errors in separating different representations, and to the formation of improper associations between representations, which can be manifested as cognitive disorganization characteristic of psychosis. NMDA receptor antagonists induce psychotic symptoms in humans (Krystal et al., 1994; Adler et al., 1998, 1999; Lahti et al., 1995, 2001) and are used as a model of psychosis in experimental animals (Stuchlik et al., 2004; Lobellova et al., 2013; Zemanova et al., 2013, Svoboda et al., 2015). Studies in rat models have also confirmed the detrimental effect of NMDA receptor antagonists on cognitive organization; for
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Corresponding author. Correspondence to: A. Stuchlík, Department of Neurophysiology of Memory, Czech Academy of Sciences, Vídeňská 1083, 142 20 Prague 4, Czech Republic. E-mail addresses: [email protected] (E. Kelemen), [email protected] (A. Stuchlík). 1 These authors contributed equally to this work. ⁎⁎
http://dx.doi.org/10.1016/j.pnpbp.2017.09.013 Received 29 March 2017; Received in revised form 15 September 2017; Accepted 16 September 2017 0278-5846/ © 2017 Elsevier Inc. All rights reserved.
Please cite this article as: Szczurowska, E., Progress in Neuropsychopharmacology & Biological Psychiatry (2017), http://dx.doi.org/10.1016/j.pnpbp.2017.09.013
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the detected signals was confirmed, signals were recorded for 60 min before, and at least 120 min after MK-801 injection. Heart rate (ECG) and temperature were monitored throughout the experiment. The body temperature of the animals was maintained at 36–37 °C during all of the procedures. The electrophysiological signal was amplified 5000–10,000 times. The signal from single units was filtered between 300 and 9000 Hz, and digitized at ~32 kHz (Neuralynx, Bozeman, MT, USA and Power1401, Cambridge Electronic Design, Cambridge, UK). The local field potential (LFP) signal was filtered between 0.1 and 500 Hz and digitized at ~2 kHz. Action potential waveforms (2 ms duration) and continuous LFP data were stored and analyzed offline. Discrimination of units was performed based on spike amplitude and wave shape using clustering algorithms in the Spike2 software (Cambridge Electronic Design, Cambridge, UK). Data from 1 h before MK-801 administration and 1 h after the administration were analyzed.
example, in hippocampus-dependent spatial tasks that required animals to segregate and coordinate two types of information (e.g., information about their position in the room and information about their position on the arena: Cimadevilla et al., 2000) NMDA receptor antagonists such as MK-801 impaired acquisition (Stuchlík and Vales, 2005; Kubík et al., 2014) as well as reversal learning (Lobellova et al., 2013; Svoboda et al., 2015). Within the framework of the discoordination hypothesis we set out to test the effects of MK-801 on the activity of hippocampal neurons. To study effects of MK-801 on hippocampal neuronal dynamics, whilst eliminating confounding effects on hippocampal neuronal firing associated with animal's position (O'Keefe and Dostrovsky, 1971; Muller et al., 1987), motor activity (Stuchlík and Vales, 2005), or attention (Fenton et al., 2010), we used urethane anesthesia in our experimental preparation. Although urethane affects multiple neurotransmitter receptors, including glutamate receptors, the effect is only mild at anesthetic doses (Hara and Harris, 2002), and unlike other anesthetics, it preserves an activity pattern resembling that of an awake animal, including hippocampal theta rhythm (Fox et al., 1986). We predicted that MK-801 would alter the coordination of the timing of neuronal firing (without affecting the firing rates of the neurons), and that this discoordination would be manifested as changes in the timing of action potentials between pairs of cells, changes in coordinated firing within cell assemblies, or changes of phase-locked firing of neurons relative to ongoing theta field oscillations.
2.4. Correlation analysis Coactivity between pairs of neurons on a timescale of tens of milliseconds was assessed by cross-correlation. Cross-correlation histograms were computed for all simultaneously recorded cell pairs at 5 ms and 10 ms resolution. Cell pairs were further studied if cross-correlation histograms contained at least 1000 events within a 500 ms time window. Correlation in firing between pairs of neurons on slower timescale was assessed using non-parametric Kendall's correlations. The cell's firing rate was calculated for 400 ms intervals and Kendall's correlation (tau) was computed for 15 min segments of recording. By comparing tau values across subsequent 15 min intervals we could assess whether positively correlated cell pairs remained positively correlated and vice versa. This way we could detect and compare spontaneous changes in pairwise coordination of neuronal discharge and changes induced by MK-801.
2. Materials and methods 2.1. Animals Six adult male Long–Evans rats (300–400 g) from the breeding colony of the Institute of Physiology of the Czech Academy of Sciences were used. Rats were housed in an air-conditioned room with stable temperature (22 ± 2 °C), humidity (60 ± 10%) and controlled 12 h/ 12 h light/dark cycle; food and water were available ad libitum. Experiments were performed during the light phase. All experimental procedures were conducted in accordance with the Animal Protection Code of the Czech Republic and the corresponding directive of the European Community Council on the use of laboratory animals (2010/ 63/EC).
2.5. Ensemble vectors and their correlations The ensemble activity during each 2 min time interval was characterized by the ensemble spike count vector of the number of action potentials that each cell emitted during the time interval (Fig. 4A). The similarity of ensemble discharge during two time intervals was quantified by computing the Pearson product-moment correlation of the two corresponding spike count vectors. To visualize the change in ensemble discharge patterns before and after MK-801 injection, a correlation matrix was constructed (Kelemen and Fenton, 2013). In the correlation matrix, each interval is compared with every other interval and the correlation coefficient is color coded (Fig. 4B).
2.2. Drug treatment MK-801 hydrogen maleate (dizocilpine; supplied by Sigma-Aldrich, Czech Republic), a non-competitive antagonist of the NMDA receptor, was dissolved in sterile physiological saline at a concentration of 0.15 mg/ml. It was injected intraperitoneally at a dose of 0.15 mg/kg. 2.3. Surgery and acute single cell recordings
2.6. Theta phase modulation
Rats were anesthetized with urethane (1.2 g/kg, intraperitoneal) and their head was fixed horizontally in a stereotaxic frame. The scalp was removed and burr holes were drilled bilaterally in the skull overlying the left and right hippocampi (AP = − 4, L ± 2.5 relative to Bregma) for tetrodes and reference electrodes. Another hole was drilled over the cerebellum for a ground electrode. Electrodes prepared from an insulated nichrome wire (25 μm in diameter, California Fine Wire, Grover Beach, CA, USA) were used in tetrode configuration. The tips of the electrode wires were cleaned by current application and then gold-plated so that their impedance was 50–200 kΩ. Two tetrodes were used during each recording session. The tips of the tetrodes were gradually advanced into the pyramidal CA1 layer of the hippocampus, until characteristic complex spike discharges were detected. Before the start of recording, the signals from neurons in the CA1 field had to remain stable for at least 30 min. After the stability of
The presence of theta oscillations in local field potentials (LFP) was assessed using the fast Fourier transformation. To study the modulation of neuronal activity by theta rhythm, the LFP signal was filtered between 3 and 10 Hz and the Hilbert transformation was used to determine the theta phase and amplitude at times of neuronal spikes. The distribution of firing in different phases of the theta rhythm was calculated for each cell (Fig. 5E). The significance of theta modulation of a cell firing was determined using runs test. For an additional analysis, each action potential of a neuron was represented by a vector with direction determined by the theta phase (Fig. 5F). The average vector was then calculated as a representation of the theta modulation of the cell's firing. The length of the average vector was used to assess the strength of theta modulation, and the direction of the vector represented the preferred theta phase of the neuron's activity. 2
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recorded 1 h before MK-801 injection (baseline recording), and the first hour following the injection. First, the cross-correlation peak close to 0 ± 50 ms was taken as an indication of the coactive firing of the two neurons (Fig. 2B). Compared to control recordings, the number of coactive cell pairs increased after MK-801 injection. This tendency was observed in pairs of CSC-CSC, and CSC-TC but not TC-TC (Fig. 2A). Coactivation in cell pairs was further quantified by calculating a coactivation index, defined as the relative magnitude of the cross-correlation value near 0 ( ± 5 ms) divided by the average cross-correlation value. Coactivation index values above 1 indicate a cross-correlation peak around the time of 0 ms and thus correlated activity (Fig. 2B) and values smaller than 1 indicate a valley in cross-correlation around 0 ms, and hence anti-correlated activity (Fig. 2C). This analysis revealed that the proportion of negatively correlated cell pairs (with a value < 1) decreased after MK-801 injection, indicating increased coactivation (control: 25.16%, MK-801: 17.74%). Using the same analysis, we further studied the effect of MK-801 on cell pairs that were previously positively correlated (before MK-801 injection), and on cell pairs that were previously anti-correlated. For previously positively correlated cell pairs, the coactivation index did not change significantly – from 2.63 (during baseline recording) to 2.64 during the first hour after MK801 injection (Mann-Whitney Rank Sum Test: All cell pairs: U = 26,638; p = 0.977; SCS-CSC: U = 14,907; p = 0.432; CSC-TC: U = 1095; p = 0.946, TC-TC: U = 18; p = 0.456 Fig. 2D). We observed that MK-801 substantially increased the coactivation index in previously anti-correlated cell pairs from 0.72 during baseline recording to 1.39 during the first hour after MK-801 injection. This increase was evident for all types of the cell pairs except TC-TC (MannWhitney Rank Sum Test, All cell pairs: U = 1715; p ≤ 0.001; SCS-CSC: U = 718; p ≤ 0.001; CSC-TC, U = 93; p = 0.004; TC-TC, U = 30; p = 0.878, Fig. 2E). Next we characterized neural coactivation on a slower timescale of 400 milliseconds. This timescale corresponds to previously reported attention-related dynamic changes in hippocampal activity (Jackson and Redish, 2007; Fenton et al., 2010). The recordings were divided into 400 ms bins and in each 15 min segment the correlation of activity between cell pairs was assessed using the non-parametric Kendall rank correlation. One-way ANOVA analysis revealed an overall significant effect of the MK-801 on correlation values (F(7, 2056) = 2.360; p = 0.021). Post hoc analysis indicated a significant increase in correlation around 60 min after injection (Tukey's test, 60 min post MK801 vs all control intervals, p's = 0.006–0.001, Fig. 3A). Tau values were highly correlated during two 15 min intervals before MK-801 injection (r = 0.817, Fig. 3B). Therefore under control conditions, positively correlated cells pairs remained positively correlated and vice versa, showing stable pairwise coordination of neuronal discharge. The stable coordination of discharge was altered by MK-801 injection. Tau values during 15 min before and 15 min after the injection were less correlated (r = 0.443, Fig. 3B). This shows that MK-801 changed pairwise coordination of neuronal discharge within the hippocampal network.
2.7. Statistical analysis Differences in parameters characterizing neuronal discharge at different time intervals before and after MK-801 injection were analyzed using t-test or one-way Analysis of Variance (ANOVA) as appropriate. ANOVA was followed by Tukey's post-hoc comparisons. Mann-Whitney Rank Sum Test was used when the condition of normal distribution of the dependent variable was not met. For statistical analysis, SigmaPlot software was used (Systat Software, Inc., San Jose, CA, USA). Data are presented in the text and figures as mean ± SEM. 3. Results 3.1. Effect of MK-801 on firing rate We recorded 56 neurons from the CA1 subfield of the dorsal hippocampi of six rats. The neurons were sorted into 47 complex spike cells (CSCs) – putative pyramidal neurons, and 9 theta cells (TCs) – putative interneurons, using standard criteria (Ranck, 1973; Kelemen and Fenton, 2010). Average firing rate was calculated and compared between 30 min intervals of the recording. MK-801 did not cause significant changes in average firing rates in either group of neurons (Mann-Whitney Rank Sum Test; CSCs: U = 1009–1061; p = 0.934–0.473; TCs: U = 31–34; p = 0.596–0.427, Fig.1A). Although the average firing rate did not change after MK-801, the compound could have caused an increase in firing rate in some neurons and a decrease in others, changes that would not manifest in the population average. To assess this possibility we calculated for each neuron the relative change in firing rate between subsequent 30 min intervals using firing rate overlap (Leutgeb et al., 2004) calculated by dividing the smaller of the two firing rates by the higher one (Fig. 1.B). The overlap between firing rates during different intervals before, during, and after MK-801 injection (~ 0.6) was comparable to the values observed in place cell firing between two exposures of a rat to two identical environments (Leutgeb et al., 2004, 2006), indicating high stability of firing rates in our experiment. The magnitudes of changes in firing rates between pairs of 30 min intervals recorded before, during, and after MK-801 injection were not significantly different (for CSCs: ANOVA, F(2, 140) = 1.454, p = 0.237; for TCs: ANOVA, F (Adler et al., 1998; Kelemen & Fenton, 2010) = 1.202, p = 0.318). A tendency, although non-significant, of increased overlap (stability) in firing rates across MK-801 injection was observed for putative TCs, which further supports the observation of stable firing after MK-801 injection. 3.2. Effect of MK-801 on correlated activity in pairs of cells The discoordination hypothesis predicts that the effect of MK-801 should primarily manifest as changes in coordinated activity between cells, rather than in firing rate changes. To assess neuronal coordination on the physiologically relevant time scale of tens of milliseconds, we calculated cross-correlations between simultaneously recorded pairs of neurons. For each cell pair, cross-correlations were computed from data
Fig. 1. Effect of MK-801 on neuronal firing rate. (A) The average firing rate of complex spike cells (CSCs) and theta cells (TCs) did not change significantly after the MK-801 injection (0 denotes time of injection, also marked by an arrow). (B) Firing rate overlap during subsequent 30 min intervals of the recording. MK-801-induced changes in firing rate between 30 min time intervals before and after injection ((− 30, 0) vs (0, 30)) were not significantly different than “spontaneous” changes in firing rate occurring before MK-801 administration ((− 60, − 30) vs (−30, 0)). Data are presented as mean ± SEM.
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Fig. 2. Effect of MK-801 on pairwise coordination of neuronal firing. (A) The percentage of coactive cell pairs (i.e., cell pairs with cross-correlation peak within 0 ± 50 ms time interval) increased after MK-801. (B, C) Examples of cross-correlation plots for cell pairs with positively correlated firing (B) and negatively correlated firing (C). (D) MK801 did not change the coactivation index in previously positively correlated neuronal pairs. (E) MK-801 increased coactivation in previously negatively correlated cell pairs. The number of cell pairs is shown in parentheses. ***Indicates p ≤ 0.001 difference from control recording (Mann-Whitney Rank Sum Test). Data are presented as mean ± SEM.
stable baseline ensemble activity (Fig. 4C). 2) To assess the change in ensemble firing induced by the MK-801 injection, we calculated average z-transformed correlation coefficient between each pre-injection interval and each post-injection interval (corresponding to the lower right quadrant of the correlation matrix). The average correlation between baseline interval and interval after MK-801 was lower (z = 0.518), indicating a significant change in ensemble activity induced by MK-801 (T = 5.3, df = 4, p = 0.006, Fig. 4C). 3) To assess stability of firing after injection, we calculated an average of the correlation coefficient (r) of all pairs of post injection time intervals (corresponding to the upper right quadrant of the correlation matrix). A high correlation (z = 1.451) value indicates stable firing after the injection. The average correlation of all interval pairs after the injection was not significantly higher than the average correlation of all interval pairs before injection (T = 1.8, df = 4, p = 0.14, Fig. 4C). Since the average firing rate of the cells remained stable throughout the recording (Fig. 1), the observed changes in the ensemble vector correlation indicate altered temporal organization of the cells' firing, rather than a change in the firing rate.
3.3. Effect of MK-801 on coordination of CA1 ensemble activity Changes of activity in entire ensembles (8–16 simultaneously recorded neurons) were characterized next. Each recording was divided into 2 min intervals and in each interval the ensemble activity was characterized by a spike count vector representing spike counts for each neuron of the ensemble (Fig. 4A). The activity across different intervals was compared by computing the Pearson correlation of corresponding ensemble vectors. The changes in activity throughout a single recording session were then displayed in a correlation matrix (Fig. 4B). The correlation matrices for the five recordings with eight or more simultaneously recorded neurons indicated highly similar ensemble activity during intervals before MK-801 injection and much lower similarity between pre-injection and post-injection intervals. This indicates a change in the ensemble activity pattern in conditions with MK-801. To quantify changes in the ensemble activity, we made the following three complementary analyses: 1) To assess stability of firing in baseline conditions, we calculated an average of the z-transformed correlation coefficient (r) for all pairs of firing rate vectors prior to injection (corresponding to the lower left quadrant of the correlation matrix on Fig. 4B). As expected, the high average correlation (z = 1.198) before injection indicates
Fig. 3. Effect of MK-801 on coactivation of cell pairs on a timescale of 400 milliseconds. (A) Kendall's correlation characterizing coactivation of all of the cell pairs. ## Indicates p < 0.01 from control recording (One-Way ANOVA, Tukey's post hoc test). Data are presented as mean ± SEM. (B) Left: Pairwise correlations were stable before MK-801 injection. Scatter plot shows that tau values were highly correlated during two 15-min intervals before MK-801 injection (tau during the interval of (− 15, 0) minutes is shown on abscissa and tau during interval (−30, − 15) minutes is shown on ordinate). Right: The coordination between neuronal firing changed after MK-801 administration. Tau values during 15 min before (− 15, 0) and 15 min after (0, 15) the injection were less correlated.
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Fig. 4. Effect of MK-801 injection on neuronal ensemble activity. (A) Example of an ensemble of nine simultaneously recorded cells. Cell ensemble activity within 2 min time intervals was characterized by ensemble vectors. (B) Correlation matrix shows Pearson's correlation coefficient for any given pair of firing rate vectors. High similarity (high correlation coefficient) is indicated by predominantly yellow pixels and low similarity by dark blue pixels. The depicted correlation matrix characterizes an example recording with particularly strong effect of MK-801 on neuronal coordination. (C) Average of the z-transformed correlation coefficient (r) for any given pair of firing rate vectors prior to injection (−60, 0) allowed the assessment of the stability of firing during baseline conditions. The average z-transformed correlation coefficient of each preinjection interval with each post-injection interval (−60, 0) vs (0, 60) allowed the assessment of the change in ensemble firing induced by MK-801 injection. The average ztransformed correlation coefficient of all pairs of post injection time intervals (0, 60) characterizes stability of firing after injection. (**Indicates p < 0.01, ***p < 0.005, NS not significant). Data are presented as mean ± SEM. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
significantly modulated by theta increased to 60.0% (Fig. 5G). The distribution of preferred theta phases for neurons with significant theta modulation is shown in Fig. 5I. There was a tendency of CSCs to initially fire on a descending phase of theta, while TCs fired more frequently at the peak of theta. There was no significant influence of MK801 on theta phase preference (Fig. 5I). We further focused on cells with significantly theta modulated firing, and we quantified the strength of modulation in these cells using a theta phase modulation vector (Fig. 5F), as described in Materials and Methods. We observed no change in the length of theta phase modulation vector after an MK-801 injection (T = 0.25, df = 24, p = 0.80, Fig. 5H). This analysis suggests that while MK-801 increased the number of theta modulated neurons, it had no significant effect on the strength of theta modulation in significantly modulated cells.
3.4. Effect of MK-801 on theta modulation of hippocampal single cell discharge Effects of NMDA receptor antagonists on theta rhythm (as well as other biologically significant oscillations) were previously thoroughly characterized (Kiss et al., 2013; Lemercier et al., 2017). In this work we focus on activity of single hippocampal units with respect to the theta rhythm in three recordings with the best quality of LFP signal (33 neurons). Under urethane anesthesia, hippocampal LFP was characterized by periods of theta activity interrupted by periods of slow, low frequency activity (Kiss et al., 2013). To identify the theta intervals, we calculated theta score as the mean FFT power at the range of theta under urethane (3–6 Hz) relative to the mean power at lower (2–3 Hz) and higher (8–10 Hz) frequencies. A theta score higher than 1.3 allowed us to separate the theta state from state with slow activity (Fig. 5A,B). Analysis of LFP signal using FFT showed a peak at ~4 Hz that persisted before and after MK-801 injection (Fig. 5C). Theta rhythm was quantified using modal frequency in theta range, power at theta peak frequency and Q factor (Lemercier et al., 2017; Fig. 5D). None of these parameters were significantly changed after MK-801 injection compared to control recording (Mann-Whitney Rank Sum Test, in all cases U's ≥ 2, p's > 0.4). Modulation of single unit activity by theta rhythm before and after MK-801 injection was characterized during theta intervals (Fig. 5A). MK-801 caused an increase in the proportion of neurons that were significantly modulated by theta rhythm. During baseline conditions, the firing rate of 51.5% neurons was significantly modulated by the theta phase as determined by runs test (Fig. 5G). During the first hour following the MK-801 injection, the number of the neurons that were
4. Discussion 4.1. Summary The neural discoordination hypothesis of schizophrenia postulates that disturbance in temporal organization of neuronal activity leads to disruption in information processing in the brain (Uhlhaas and Singer, 2006; Jones, 2010; Moghaddam and Wood, 2014; Fenton, 2009, 2015). In the context of this hypothesis, we set out to test directly the effect of the psychotomimetic substance dizocilpine (MK-801) on the activity of hippocampal neurons in rats under urethane anesthesia. We found that MK-801 did not affect the firing rate of complex spike cells (putative pyramidal neurons) or theta cells (putative interneurons). MK-801, however, did change the temporal organization of the neuronal 5
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Fig. 5. Effect of MK-801 on the theta modulation of neuronal discharge. (A) Characterizing LFP using time frequency analysis reveals intervals of theta oscillation (~4 Hz) interrupted by intervals of dominant slow oscillations (0.5–4 Hz). Detected theta intervals are marked by the blue band under the time-frequency plot. (Example from a control recording). (B) Examples of two-second intervals of LFP with predominant theta (upper trace) and non-theta (lower trace) from recording in panel A. (C) Power spectra of LFP before and after MK-801 injection. Average power spectra of theta intervals during three recordings are shown. (D) Quantification of effects of MK-801 on theta rhythm using parameters of relative peak power, peak theta frequency, and Q factor. (E) An example of theta phase modulation of a complex spike cell firing before and after MK-801 injection. (F) Polar graphs showing theta phase modulation of neuronal firing for the same cell as in E. Small black arrow indicates average vector length and angle. (G) Percentage of the cells significantly modulated by theta phase. The significance of theta modulation was determined using runs test. (H) Strength of modulation by theta in significantly modulated cells assessed by average length of theta vector. MK-801 had no significant effect on vector length. (I) Theta phase preference in neuronal firing is expressed as average theta vector direction. Data are presented as mean ± SEM. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
coactivated neurons participate in the encoding of the same representations while neurons that do not fire together encode different representations (Harris et al., 2003; Jezek et al., 2011; Kelemen and Fenton, 2010, 2016). In this framework, increased coactivation between originally discoordinated cell pairs caused by the application of MK-801 indicates improper coactivation of distinct representations that should be separate (Fig. 6). Increased correlations between originally anti-correlated cell pairs might reflect easier transitions between different attractor states within hippocampal network. This way, MK-801 may interfere with naturally occurring hippocampal pattern separation processes (Rolls, 2016). Impairment in pattern separation is consistent with the hypothesized instability of spontaneous activity states in schizophrenia, which makes cortical networks prone to noise-induced jumps to a different attractor activation state (Rolls, 2012; Lerner et al., 2012). This finding extends earlier observations of increased similarity between hippocampal ensemble representations of distinct environments after MK-801, assessed by immediate early gene expression (Kubík et al., 2014). The MK-801 injection led to an increase in the number of neurons
discharge that we observed on the level of neuronal pairs, as well as on the level of neuronal multi-unit ensembles. Specifically, MK-801 increased coactivation (coincident firing) between pairs of neurons. This increase was driven by coactivation of originally anti-correlated neuronal pairs, rather than increased coactivation of originally coactive neurons. The changes in neuronal coordination were also detected in the activity of neuronal ensembles. Our observations confirmed the disorganization of neuronal discharge under the influence of psychotomimetic MK-801.
4.2. Increased neuronal coactivation after dizocilpine injection MK-801 injection led to an increase in the number of coactive cell pairs, i.e., cell pairs with a cross-correlation peak at ± 50 ms. The increase in coactivation after injection was mostly a consequence of coactivated firing of cell pairs that were anti-correlated before the injection (Fig. 2E). Coordination of neuronal firing at this timescale is crucial for information processing in the hippocampus (Harris et al., 2003). Consistent with the cell assembly hypothesis (Hebb, 1949), 6
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is toggling between the room and arena representations (Kelemen and Fenton, 2010). Switching between different hippocampal spatial representations is organized by the theta rhythm (Jezek et al., 2011). The oversynchronization of neurons in this task would lead to inadequate separation of the neuronal representations of the two sets of landmarks, which would result in bad behavioral decisions and impaired performance in the task. Impairment in the carousel task was indeed observed after application of MK-801 (Stuchlík and Vales, 2005; Kubík et al., 2014; Svoboda et al., 2015), after application of an alternative NMDA receptor antagonist phencyclidine (Kao et al., 2013), as well as in a neurodevelopmental model of schizophrenia (Lee et al., 2012; O'Reilly et al., 2014). Synaptic plasticity related to memory storage depends on the precise timing of neuronal activation. Discoordination in timing of activity between neurons should thus lead to improper long-term plasticity, and consequently to slower learning. Memory deficits are indeed observed in human patients with schizophrenia (Ranganath et al., 2008). Synaptic plasticity is impaired in rodent models of the disease (Wöhrl et al., 2007; Wiescholleck and Manahan-Vaughan, 2013), and memory deficits were observed in the place avoidance task in rats after MK-801 application (Stuchlík and Vales, 2005; Kubík et al., 2014).
Fig. 6. Hypothetical model of neural coordination deficit after MK-801 administration. (A) Under normal conditions the discharge of hippocampal neurons is organized into coactivated cell assemblies. The illustration depicts a red cell assembly and a blue cell assembly, each active at different cycles of the theta rhythm (middle). Each cell assembly represents a concept – letters A and B in our example (bottom). (B) After MK-801 administration the neural coordination is impaired. Neurons that originally did not discharge at the same time start to discharge together. The cell assemblies that were previously activated separately become disorganized and merged together, and the information that they encoded is lost. Such neuronal coordination deficit can manifest itself behaviorally as impairment in cognitive control. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
4.4. Mechanism of NMDAR antagonist effect on neuronal coordination While the mechanism leading to neural discoordination in our model remains unknown, we can speculate that the effect is mediated by alternation in the activity of GABA-ergic interneurons. NMDA-receptor hypofunction on parvalbumin-expressing inhibitory neurons was suggested as a pathological mechanism in schizophrenia (Jadi et al., 2016), which is consistent with observations of defects in GABA-ergic neurotransmission in the hippocampus, prefrontal and anterior cingulate cortices in human patients with schizophrenia, and in animal models (Benes and Berretta, 2001; Lewis et al., 2005; Homayoun and Moghaddam, 2007; Benes, 2015; Grüter et al., 2015). Alternations in GABA-ergic function lead to excitation-inhibition discoordination (Dragoi and Buzsáki, 2005; Fenton, 2015), which can lead directly to discoordination of neuronal cell assemblies and can also manifest as abnormal neuronal oscillatory activity (Lee et al., 2012, 2014; Kiss et al., 2013; Jadi et al., 2016; Lemercier et al., 2017). Discoordination in activity leads to impaired neuronal communication not only locally, i.e., within a brain structure such as the hippocampus, but also between different brain structures. Discoordination of neuronal activity between CA1 and CA3 sub-regions of the hippocampus after MK-801 administration was observed using immediate early gene expression (Buchtová et al., 2017), and discoordination of activity between two contralateral hippocampi was observed by local field potential measurements (Lee et al., 2012, 2014).
with firing that was significantly modulated by the theta rhythm, while the injection had no effect on theta power itself. Theta rhythm has a strong influence on organizing neuronal discharge in the hippocampus in both awake and urethane anesthetized rats (Fox et al., 1986). The phase of neuronal discharge within theta wave carries behaviorally relevant information (O'Keefe and Recce, 1993, Huxter et al., 2003; Dragoi and Buzsáki, 2006). From the perspective of the neural coordination hypothesis of schizophrenia, the role of theta in organizing activity corresponding to alternative competing representations is particularly interesting (Harris et al., 2003; Jezek et al., 2011). The relevance of theta to pathology in schizophrenia is suggested by decreased theta phase synchrony between the two contralateral hippocampi in a developmental model of the disease (Lee et al., 2012), and is further supported by the fact that compensation of cognitive deficits in the model is accompanied by increased theta synchrony (Lee et al., 2014). In light of these findings, the increased theta modulation of neuronal discharge indicates disrupted coordination between distinct hippocampal representations.
4.3. From neuronal oversynchronisation to cognitive discoordination 5. Conclusions The disorganized neuronal discharge identified in our study may lead to a cognitive coordination deficit, which is prominent among the cognitive symptoms of schizophrenia and psychosis. Cognitive coordination is manifested as the ability to distinguish between relevant and irrelevant information, and the ability to use the former and ignore the latter in response to actual behavioral situations. The effect of psychotomimetic manipulations on cognitive coordination has been studied in animal models using a place avoidance task on a rotating carousel (Stuchlik et al., 2004; Lee et al., 2012). In this task, the rat is exposed to two sets of environmental landmarks – stationary room landmarks and rotating arena landmarks, and must use the relevant set and ignore the irrelevant one according to behavioral circumstances. Under these conditions, the hippocampal neurons that are simultaneously active at any particular moment tend to represent the same set of landmarks (either arena, or room) and the ensemble activation state
The inadequate treatment options available for schizophrenia, especially its cognitive symptoms, and the slow progress in development of new treatment strategies (Lieberman et al., 2005; Abbott, 2010) emphasize the importance of understanding the neuronal mechanisms of the disorder. We tested the discoordination hypothesis of psychosis by recording activity of hippocampal neuronal ensembles after administration of psychotomimetic MK-801. MK-801 led to increased coactivation of firing between neurons. The increase was primarily driven by increases in coincident firing between previously anti-correlated cells. Increased pairwise coactivation manifested as disorganized discharge on the level of neuronal ensembles, which in turn could lead to disorganization in information processing. Disorganization of neuronal activity after a psychotomimetic dose of MK-801 supports the discoordination hypothesis of psychosis. 7
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Grant support This research was mainly funded by the Czech Science Foundation grant 17-04047S. Institutional support for Institute of Physiology, Czech Academy of Sciences was provided by RVO: 67985823. Institutional support for NIMH was provided by the project “Sustainability for the National Institute of Mental Health”, under grant number LO1611, with financial support from the Ministry of Education, Youth and Sports of the Czech Republic, under the NPU I program. Partly supported by ERDF project, OPPK Mikroskopický systém. Ethical statement All experimental procedures were conducted in accordance with the Animal Protection Code of the Czech Republic and the corresponding directive of the European Community Council on the use of laboratory animals (2010/63/EC). Authors have no conflict of interests. Acknowledgements We thank Daniel Klement and David Levčík for their contribution to the early practical and theoretical development of this study, to Ehud Kaplan and Rachel R. Horsley for useful comments on a previous version of the manuscript and to Rachel R. Horsley also for proofreading the manuscript. References Abbott, A., 2010. The drug deadlock. Nature 468, 158–159. http://dx.doi.org/10.1038/ 468158a. Adler, C.M., Goldberg, T.E., Malhotra, A.K., Pickar, D., Breier, A., 1998. Effects of ketamine on thought disorder, working memory, and semantic memory in healthy volunteers. Biol. Psychiatry 43 (11), 811–816. Adler, C.M., Malhotra, A.K., Elman, I., Goldberg, T.E., Egan, M., Pickar, D., Breier, A., 1999. Comparison of ketamine-induced thought disorder in healthy volunteers and thought disorder in schizophrenia. Am. J. Psychiatry 156, 1646–1649. Benes, F.M., 2015. Building models for postmortem abnormalities in hippocampus of schizophrenics. Schizophr. Res. 167, 73–83. http://dx.doi.org/10.1016/j.schres. 2015.01.014. Benes, F.M., Berretta, S., 2001. GABAergic interneurons: implications for understanding schizophrenia and bipolar disorder. Neuropsychopharmacology 25, 1–27. Buchtová, H., Fajnerová, I., Stuchlík, A., Kubík, Š., 2017. Acute systemic MK-801 induced functional uncoupling between hippocampal areas CA3 and CA1 with distant effect in the retrosplenial cortex. Hippocampus 27, 134–144. http://dx.doi.org/10.1002/hipo. 22678. Cimadevilla, J.M., Fenton, A.A., Bures, J., 2000. Functional inactivation of dorsal hippocampus impairs active place avoidance in rats. Neurosci. Lett. 285 (1), 53–56. Dragoi, G., Buzsáki, G., 2005. Cortical inhibitory neurons and schizophrenia. Nat. Rev. Neurosci. 6 (4), 312–324. Dragoi, G., Buzsáki, G., 2006. Temporal encoding of place sequences by hippocampal cell assemblies. Neuron 50 (1), 145–157. Engel, A.K., König, P., Singer, W., 1991. Direct physiological evidence for scene segmentation by temporal coding. Proc. Natl. Acad. Sci. U. S. A. 88 (20), 9136–9140. Fenton, A.A., 2009. Neural coordination and psychotic disorganization. In: Holscher, C., Munk, M.H. (Eds.), Information Processing by Neuronal Populations. Cambridge University Press, London, pp. 387–408. Fenton, A.A., 2015. Excitation-inhibition discoordination in rodent models of mental disorders. Biol. Psychiatry 77, 1079–1088. Fenton, A.A., Lytton, W.W., Barry, J.M., Lenck-Santini, P.P., Zinyuk, L.E., Kubík, S., Bures, J., Poucet, B., Muller, R.U., Olypher, A.V., 2010. Attention-like modulation of hippocampus place cell discharge. J. Neurosci. 30 (13), 4613–4625. http://dx.doi.org/ 10.1523/JNEUROSCI.5576-09.2010. Fox, S.E., Wolfson, S., Ranck Jr., J.B., 1986. Hippocampal theta rhythm and the firing of neurons in walking and urethane anesthetized rats. Exp. Brain Res. 62 (3), 495–508. Grüter, T., Wiescholleck, V., Dubovyk, V., Aliane, V., Manahan-Vaughan, D., 2015. Altered neuronal excitability underlies impaired hippocampal function in an animal model of psychosis. Front. Behav. Neurosci. 9, 117. http://dx.doi.org/10.3389/ fnbeh.2015.00117. Hara, K., Harris, R.A., 2002. The anesthetic mechanism of urethane: the effects on neurotransmitter-gated ion channels. Anesth. Analg. 94 (2), 313–318. Harris, K.D., Csicsvari, J., Hirase, H., Dragoi, G., Buzsáki, G., 2003. Organization of cell assemblies in the hippocampus. Nature 424 (6948), 552–556. Hebb, D., 1949. The Organization of Behavior. Wiley & Sons, New York. Homayoun, H., Moghaddam, B., 2007. NMDA receptor hypofunction produces opposite effects on prefrontal cortex interneurons and pyramidal neurons. J. Neurosci. 27 (43), 11496–11500. Huxter, J., Burgess, N., O'Keefe, J., 2003. Independent rate and temporal coding in
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Progress in Neuropsychopharmacology & Biological Psychiatry journal homepage: www.elsevier.com/locate/pnp
Impairment of neural coordination in hippocampal neuronal ensembles after a psychotomimetic dose of dizocilpine Ewa Szczurowskac,1, Nikhil Ahujaa,1, Přemysl Jiruškab, Eduard Kelemenc,⁎, Aleš Stuchlíka,⁎⁎ a b c
Department of Neurophysiology of Memory and Institute of Physiology, Czech Academy of Sciences, Vídeňská 1083, 142 20 Prague, Czech Republic Department of Developmental Epileptology, Institute of Physiology, Czech Academy of Sciences, Vídeňská 1083, 142 20 Prague, Czech Republic National Institute of Mental Health, Topolová 748, 250 67 Klecany, Czech Republic
A R T I C L E I N F O
A B S T R A C T
Keywords: Psychosis MK-801 Neuronal discoordination Hippocampus Theta rhythm
The discoordination hypothesis of schizophrenia posits discoordination of neural activity as the central mechanism that underlies some psychotic symptoms (including ‘hallmark’ cognitive symptoms) of schizophrenia. To test this proposition, we studied the activity of hippocampal neurons in urethane anesthetized Long Evans rats after 0.15 mg/kg dizocilpine (MK-801), an N-Methyl-D-aspartate (NMDA) glutamate receptor antagonist, which can cause psychotic symptoms in humans and cognitive control impairments in animals. We observed that MK-801 altered the temporal coordination, but not rate, of neuronal firing. Coactivation between neurons increased, driven primarily by increased coincident firing of cell pairs that did not originally fire together before MK-801 injection. Increased pairwise coactivation manifested as disorganized discharge on the level of neuronal ensembles, which in turn could lead to disorganization in information processing. Disorganization of neuronal activity after a psychotomimetic dose of MK-801 supports the discoordination hypothesis of psychosis.
1. Introduction According to the discoordination hypothesis of schizophrenia, discoordination of activity within neuronal networks is the neural mechanism that underlies symptoms of cognitive disorganization (Phillips and Silverstein, 2003). Central to this hypothesis is the concept of cell assemblies: coalitions of coactive cells that constitute the fundamental functional units of information encoding and processing in the brain (Hebb, 1949; Harris et al., 2003). The importance of coordination in neuronal firing for information processing is supported by a substantial body of evidence; for example, synchronization of neuronal activity in the visual cortex has been implicated in visual scene segmentation (Engel et al., 1991), and coordinated activation of cell assemblies in the hippocampus was shown to organize distinct, competing spatial representations (Kelemen and Fenton, 2010, 2016; Jezek et al., 2011). Organization of neuronal activity has been characterized on timescales ranging from tens of milliseconds to seconds (Olypher et al., 2002; Harris et al., 2003; Jezek et al., 2011; Kelemen and Fenton, 2010, 2013). The timing of synchronized neuronal firing within cell assemblies is organized by rhythmical gamma (Engel et al., 1991; Harris et al., 2003) and theta activity (O'Keefe and Recce, 1993; Dragoi and Buzsáki, 2006; Jezek et al., 2011).
Disturbances in the temporal organization of neuronal firing are the hypothesized cause of impaired information processing in mental disorders, including schizophrenia (Uhlhaas and Singer, 2006; Olypher et al., 2006; Jones, 2010; Moghaddam & Wood, 2014; Fenton, 2015). According to this hypothesis, psychosis is related to impairments in coordination of activity between neurons – a phenomenon that we call discoordination. Discoordination would occur for example, if neurons that normally discharge together stopped firing together, or if neurons that normally discharge separately started firing together, or if combination of the two effects occurred. In this theoretical framework, discoordination manifests on the cognitive level by incomplete, fuzzy or otherwise damaged mental representations. Discoordination in firing between different cell assemblies leads to errors in separating different representations, and to the formation of improper associations between representations, which can be manifested as cognitive disorganization characteristic of psychosis. NMDA receptor antagonists induce psychotic symptoms in humans (Krystal et al., 1994; Adler et al., 1998, 1999; Lahti et al., 1995, 2001) and are used as a model of psychosis in experimental animals (Stuchlik et al., 2004; Lobellova et al., 2013; Zemanova et al., 2013, Svoboda et al., 2015). Studies in rat models have also confirmed the detrimental effect of NMDA receptor antagonists on cognitive organization; for
⁎
Corresponding author. Correspondence to: A. Stuchlík, Department of Neurophysiology of Memory, Czech Academy of Sciences, Vídeňská 1083, 142 20 Prague 4, Czech Republic. E-mail addresses: [email protected] (E. Kelemen), [email protected] (A. Stuchlík). 1 These authors contributed equally to this work. ⁎⁎
http://dx.doi.org/10.1016/j.pnpbp.2017.09.013 Received 29 March 2017; Received in revised form 15 September 2017; Accepted 16 September 2017 0278-5846/ © 2017 Elsevier Inc. All rights reserved.
Please cite this article as: Szczurowska, E., Progress in Neuropsychopharmacology & Biological Psychiatry (2017), http://dx.doi.org/10.1016/j.pnpbp.2017.09.013
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the detected signals was confirmed, signals were recorded for 60 min before, and at least 120 min after MK-801 injection. Heart rate (ECG) and temperature were monitored throughout the experiment. The body temperature of the animals was maintained at 36–37 °C during all of the procedures. The electrophysiological signal was amplified 5000–10,000 times. The signal from single units was filtered between 300 and 9000 Hz, and digitized at ~32 kHz (Neuralynx, Bozeman, MT, USA and Power1401, Cambridge Electronic Design, Cambridge, UK). The local field potential (LFP) signal was filtered between 0.1 and 500 Hz and digitized at ~2 kHz. Action potential waveforms (2 ms duration) and continuous LFP data were stored and analyzed offline. Discrimination of units was performed based on spike amplitude and wave shape using clustering algorithms in the Spike2 software (Cambridge Electronic Design, Cambridge, UK). Data from 1 h before MK-801 administration and 1 h after the administration were analyzed.
example, in hippocampus-dependent spatial tasks that required animals to segregate and coordinate two types of information (e.g., information about their position in the room and information about their position on the arena: Cimadevilla et al., 2000) NMDA receptor antagonists such as MK-801 impaired acquisition (Stuchlík and Vales, 2005; Kubík et al., 2014) as well as reversal learning (Lobellova et al., 2013; Svoboda et al., 2015). Within the framework of the discoordination hypothesis we set out to test the effects of MK-801 on the activity of hippocampal neurons. To study effects of MK-801 on hippocampal neuronal dynamics, whilst eliminating confounding effects on hippocampal neuronal firing associated with animal's position (O'Keefe and Dostrovsky, 1971; Muller et al., 1987), motor activity (Stuchlík and Vales, 2005), or attention (Fenton et al., 2010), we used urethane anesthesia in our experimental preparation. Although urethane affects multiple neurotransmitter receptors, including glutamate receptors, the effect is only mild at anesthetic doses (Hara and Harris, 2002), and unlike other anesthetics, it preserves an activity pattern resembling that of an awake animal, including hippocampal theta rhythm (Fox et al., 1986). We predicted that MK-801 would alter the coordination of the timing of neuronal firing (without affecting the firing rates of the neurons), and that this discoordination would be manifested as changes in the timing of action potentials between pairs of cells, changes in coordinated firing within cell assemblies, or changes of phase-locked firing of neurons relative to ongoing theta field oscillations.
2.4. Correlation analysis Coactivity between pairs of neurons on a timescale of tens of milliseconds was assessed by cross-correlation. Cross-correlation histograms were computed for all simultaneously recorded cell pairs at 5 ms and 10 ms resolution. Cell pairs were further studied if cross-correlation histograms contained at least 1000 events within a 500 ms time window. Correlation in firing between pairs of neurons on slower timescale was assessed using non-parametric Kendall's correlations. The cell's firing rate was calculated for 400 ms intervals and Kendall's correlation (tau) was computed for 15 min segments of recording. By comparing tau values across subsequent 15 min intervals we could assess whether positively correlated cell pairs remained positively correlated and vice versa. This way we could detect and compare spontaneous changes in pairwise coordination of neuronal discharge and changes induced by MK-801.
2. Materials and methods 2.1. Animals Six adult male Long–Evans rats (300–400 g) from the breeding colony of the Institute of Physiology of the Czech Academy of Sciences were used. Rats were housed in an air-conditioned room with stable temperature (22 ± 2 °C), humidity (60 ± 10%) and controlled 12 h/ 12 h light/dark cycle; food and water were available ad libitum. Experiments were performed during the light phase. All experimental procedures were conducted in accordance with the Animal Protection Code of the Czech Republic and the corresponding directive of the European Community Council on the use of laboratory animals (2010/ 63/EC).
2.5. Ensemble vectors and their correlations The ensemble activity during each 2 min time interval was characterized by the ensemble spike count vector of the number of action potentials that each cell emitted during the time interval (Fig. 4A). The similarity of ensemble discharge during two time intervals was quantified by computing the Pearson product-moment correlation of the two corresponding spike count vectors. To visualize the change in ensemble discharge patterns before and after MK-801 injection, a correlation matrix was constructed (Kelemen and Fenton, 2013). In the correlation matrix, each interval is compared with every other interval and the correlation coefficient is color coded (Fig. 4B).
2.2. Drug treatment MK-801 hydrogen maleate (dizocilpine; supplied by Sigma-Aldrich, Czech Republic), a non-competitive antagonist of the NMDA receptor, was dissolved in sterile physiological saline at a concentration of 0.15 mg/ml. It was injected intraperitoneally at a dose of 0.15 mg/kg. 2.3. Surgery and acute single cell recordings
2.6. Theta phase modulation
Rats were anesthetized with urethane (1.2 g/kg, intraperitoneal) and their head was fixed horizontally in a stereotaxic frame. The scalp was removed and burr holes were drilled bilaterally in the skull overlying the left and right hippocampi (AP = − 4, L ± 2.5 relative to Bregma) for tetrodes and reference electrodes. Another hole was drilled over the cerebellum for a ground electrode. Electrodes prepared from an insulated nichrome wire (25 μm in diameter, California Fine Wire, Grover Beach, CA, USA) were used in tetrode configuration. The tips of the electrode wires were cleaned by current application and then gold-plated so that their impedance was 50–200 kΩ. Two tetrodes were used during each recording session. The tips of the tetrodes were gradually advanced into the pyramidal CA1 layer of the hippocampus, until characteristic complex spike discharges were detected. Before the start of recording, the signals from neurons in the CA1 field had to remain stable for at least 30 min. After the stability of
The presence of theta oscillations in local field potentials (LFP) was assessed using the fast Fourier transformation. To study the modulation of neuronal activity by theta rhythm, the LFP signal was filtered between 3 and 10 Hz and the Hilbert transformation was used to determine the theta phase and amplitude at times of neuronal spikes. The distribution of firing in different phases of the theta rhythm was calculated for each cell (Fig. 5E). The significance of theta modulation of a cell firing was determined using runs test. For an additional analysis, each action potential of a neuron was represented by a vector with direction determined by the theta phase (Fig. 5F). The average vector was then calculated as a representation of the theta modulation of the cell's firing. The length of the average vector was used to assess the strength of theta modulation, and the direction of the vector represented the preferred theta phase of the neuron's activity. 2
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recorded 1 h before MK-801 injection (baseline recording), and the first hour following the injection. First, the cross-correlation peak close to 0 ± 50 ms was taken as an indication of the coactive firing of the two neurons (Fig. 2B). Compared to control recordings, the number of coactive cell pairs increased after MK-801 injection. This tendency was observed in pairs of CSC-CSC, and CSC-TC but not TC-TC (Fig. 2A). Coactivation in cell pairs was further quantified by calculating a coactivation index, defined as the relative magnitude of the cross-correlation value near 0 ( ± 5 ms) divided by the average cross-correlation value. Coactivation index values above 1 indicate a cross-correlation peak around the time of 0 ms and thus correlated activity (Fig. 2B) and values smaller than 1 indicate a valley in cross-correlation around 0 ms, and hence anti-correlated activity (Fig. 2C). This analysis revealed that the proportion of negatively correlated cell pairs (with a value < 1) decreased after MK-801 injection, indicating increased coactivation (control: 25.16%, MK-801: 17.74%). Using the same analysis, we further studied the effect of MK-801 on cell pairs that were previously positively correlated (before MK-801 injection), and on cell pairs that were previously anti-correlated. For previously positively correlated cell pairs, the coactivation index did not change significantly – from 2.63 (during baseline recording) to 2.64 during the first hour after MK801 injection (Mann-Whitney Rank Sum Test: All cell pairs: U = 26,638; p = 0.977; SCS-CSC: U = 14,907; p = 0.432; CSC-TC: U = 1095; p = 0.946, TC-TC: U = 18; p = 0.456 Fig. 2D). We observed that MK-801 substantially increased the coactivation index in previously anti-correlated cell pairs from 0.72 during baseline recording to 1.39 during the first hour after MK-801 injection. This increase was evident for all types of the cell pairs except TC-TC (MannWhitney Rank Sum Test, All cell pairs: U = 1715; p ≤ 0.001; SCS-CSC: U = 718; p ≤ 0.001; CSC-TC, U = 93; p = 0.004; TC-TC, U = 30; p = 0.878, Fig. 2E). Next we characterized neural coactivation on a slower timescale of 400 milliseconds. This timescale corresponds to previously reported attention-related dynamic changes in hippocampal activity (Jackson and Redish, 2007; Fenton et al., 2010). The recordings were divided into 400 ms bins and in each 15 min segment the correlation of activity between cell pairs was assessed using the non-parametric Kendall rank correlation. One-way ANOVA analysis revealed an overall significant effect of the MK-801 on correlation values (F(7, 2056) = 2.360; p = 0.021). Post hoc analysis indicated a significant increase in correlation around 60 min after injection (Tukey's test, 60 min post MK801 vs all control intervals, p's = 0.006–0.001, Fig. 3A). Tau values were highly correlated during two 15 min intervals before MK-801 injection (r = 0.817, Fig. 3B). Therefore under control conditions, positively correlated cells pairs remained positively correlated and vice versa, showing stable pairwise coordination of neuronal discharge. The stable coordination of discharge was altered by MK-801 injection. Tau values during 15 min before and 15 min after the injection were less correlated (r = 0.443, Fig. 3B). This shows that MK-801 changed pairwise coordination of neuronal discharge within the hippocampal network.
2.7. Statistical analysis Differences in parameters characterizing neuronal discharge at different time intervals before and after MK-801 injection were analyzed using t-test or one-way Analysis of Variance (ANOVA) as appropriate. ANOVA was followed by Tukey's post-hoc comparisons. Mann-Whitney Rank Sum Test was used when the condition of normal distribution of the dependent variable was not met. For statistical analysis, SigmaPlot software was used (Systat Software, Inc., San Jose, CA, USA). Data are presented in the text and figures as mean ± SEM. 3. Results 3.1. Effect of MK-801 on firing rate We recorded 56 neurons from the CA1 subfield of the dorsal hippocampi of six rats. The neurons were sorted into 47 complex spike cells (CSCs) – putative pyramidal neurons, and 9 theta cells (TCs) – putative interneurons, using standard criteria (Ranck, 1973; Kelemen and Fenton, 2010). Average firing rate was calculated and compared between 30 min intervals of the recording. MK-801 did not cause significant changes in average firing rates in either group of neurons (Mann-Whitney Rank Sum Test; CSCs: U = 1009–1061; p = 0.934–0.473; TCs: U = 31–34; p = 0.596–0.427, Fig.1A). Although the average firing rate did not change after MK-801, the compound could have caused an increase in firing rate in some neurons and a decrease in others, changes that would not manifest in the population average. To assess this possibility we calculated for each neuron the relative change in firing rate between subsequent 30 min intervals using firing rate overlap (Leutgeb et al., 2004) calculated by dividing the smaller of the two firing rates by the higher one (Fig. 1.B). The overlap between firing rates during different intervals before, during, and after MK-801 injection (~ 0.6) was comparable to the values observed in place cell firing between two exposures of a rat to two identical environments (Leutgeb et al., 2004, 2006), indicating high stability of firing rates in our experiment. The magnitudes of changes in firing rates between pairs of 30 min intervals recorded before, during, and after MK-801 injection were not significantly different (for CSCs: ANOVA, F(2, 140) = 1.454, p = 0.237; for TCs: ANOVA, F (Adler et al., 1998; Kelemen & Fenton, 2010) = 1.202, p = 0.318). A tendency, although non-significant, of increased overlap (stability) in firing rates across MK-801 injection was observed for putative TCs, which further supports the observation of stable firing after MK-801 injection. 3.2. Effect of MK-801 on correlated activity in pairs of cells The discoordination hypothesis predicts that the effect of MK-801 should primarily manifest as changes in coordinated activity between cells, rather than in firing rate changes. To assess neuronal coordination on the physiologically relevant time scale of tens of milliseconds, we calculated cross-correlations between simultaneously recorded pairs of neurons. For each cell pair, cross-correlations were computed from data
Fig. 1. Effect of MK-801 on neuronal firing rate. (A) The average firing rate of complex spike cells (CSCs) and theta cells (TCs) did not change significantly after the MK-801 injection (0 denotes time of injection, also marked by an arrow). (B) Firing rate overlap during subsequent 30 min intervals of the recording. MK-801-induced changes in firing rate between 30 min time intervals before and after injection ((− 30, 0) vs (0, 30)) were not significantly different than “spontaneous” changes in firing rate occurring before MK-801 administration ((− 60, − 30) vs (−30, 0)). Data are presented as mean ± SEM.
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Fig. 2. Effect of MK-801 on pairwise coordination of neuronal firing. (A) The percentage of coactive cell pairs (i.e., cell pairs with cross-correlation peak within 0 ± 50 ms time interval) increased after MK-801. (B, C) Examples of cross-correlation plots for cell pairs with positively correlated firing (B) and negatively correlated firing (C). (D) MK801 did not change the coactivation index in previously positively correlated neuronal pairs. (E) MK-801 increased coactivation in previously negatively correlated cell pairs. The number of cell pairs is shown in parentheses. ***Indicates p ≤ 0.001 difference from control recording (Mann-Whitney Rank Sum Test). Data are presented as mean ± SEM.
stable baseline ensemble activity (Fig. 4C). 2) To assess the change in ensemble firing induced by the MK-801 injection, we calculated average z-transformed correlation coefficient between each pre-injection interval and each post-injection interval (corresponding to the lower right quadrant of the correlation matrix). The average correlation between baseline interval and interval after MK-801 was lower (z = 0.518), indicating a significant change in ensemble activity induced by MK-801 (T = 5.3, df = 4, p = 0.006, Fig. 4C). 3) To assess stability of firing after injection, we calculated an average of the correlation coefficient (r) of all pairs of post injection time intervals (corresponding to the upper right quadrant of the correlation matrix). A high correlation (z = 1.451) value indicates stable firing after the injection. The average correlation of all interval pairs after the injection was not significantly higher than the average correlation of all interval pairs before injection (T = 1.8, df = 4, p = 0.14, Fig. 4C). Since the average firing rate of the cells remained stable throughout the recording (Fig. 1), the observed changes in the ensemble vector correlation indicate altered temporal organization of the cells' firing, rather than a change in the firing rate.
3.3. Effect of MK-801 on coordination of CA1 ensemble activity Changes of activity in entire ensembles (8–16 simultaneously recorded neurons) were characterized next. Each recording was divided into 2 min intervals and in each interval the ensemble activity was characterized by a spike count vector representing spike counts for each neuron of the ensemble (Fig. 4A). The activity across different intervals was compared by computing the Pearson correlation of corresponding ensemble vectors. The changes in activity throughout a single recording session were then displayed in a correlation matrix (Fig. 4B). The correlation matrices for the five recordings with eight or more simultaneously recorded neurons indicated highly similar ensemble activity during intervals before MK-801 injection and much lower similarity between pre-injection and post-injection intervals. This indicates a change in the ensemble activity pattern in conditions with MK-801. To quantify changes in the ensemble activity, we made the following three complementary analyses: 1) To assess stability of firing in baseline conditions, we calculated an average of the z-transformed correlation coefficient (r) for all pairs of firing rate vectors prior to injection (corresponding to the lower left quadrant of the correlation matrix on Fig. 4B). As expected, the high average correlation (z = 1.198) before injection indicates
Fig. 3. Effect of MK-801 on coactivation of cell pairs on a timescale of 400 milliseconds. (A) Kendall's correlation characterizing coactivation of all of the cell pairs. ## Indicates p < 0.01 from control recording (One-Way ANOVA, Tukey's post hoc test). Data are presented as mean ± SEM. (B) Left: Pairwise correlations were stable before MK-801 injection. Scatter plot shows that tau values were highly correlated during two 15-min intervals before MK-801 injection (tau during the interval of (− 15, 0) minutes is shown on abscissa and tau during interval (−30, − 15) minutes is shown on ordinate). Right: The coordination between neuronal firing changed after MK-801 administration. Tau values during 15 min before (− 15, 0) and 15 min after (0, 15) the injection were less correlated.
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Fig. 4. Effect of MK-801 injection on neuronal ensemble activity. (A) Example of an ensemble of nine simultaneously recorded cells. Cell ensemble activity within 2 min time intervals was characterized by ensemble vectors. (B) Correlation matrix shows Pearson's correlation coefficient for any given pair of firing rate vectors. High similarity (high correlation coefficient) is indicated by predominantly yellow pixels and low similarity by dark blue pixels. The depicted correlation matrix characterizes an example recording with particularly strong effect of MK-801 on neuronal coordination. (C) Average of the z-transformed correlation coefficient (r) for any given pair of firing rate vectors prior to injection (−60, 0) allowed the assessment of the stability of firing during baseline conditions. The average z-transformed correlation coefficient of each preinjection interval with each post-injection interval (−60, 0) vs (0, 60) allowed the assessment of the change in ensemble firing induced by MK-801 injection. The average ztransformed correlation coefficient of all pairs of post injection time intervals (0, 60) characterizes stability of firing after injection. (**Indicates p < 0.01, ***p < 0.005, NS not significant). Data are presented as mean ± SEM. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
significantly modulated by theta increased to 60.0% (Fig. 5G). The distribution of preferred theta phases for neurons with significant theta modulation is shown in Fig. 5I. There was a tendency of CSCs to initially fire on a descending phase of theta, while TCs fired more frequently at the peak of theta. There was no significant influence of MK801 on theta phase preference (Fig. 5I). We further focused on cells with significantly theta modulated firing, and we quantified the strength of modulation in these cells using a theta phase modulation vector (Fig. 5F), as described in Materials and Methods. We observed no change in the length of theta phase modulation vector after an MK-801 injection (T = 0.25, df = 24, p = 0.80, Fig. 5H). This analysis suggests that while MK-801 increased the number of theta modulated neurons, it had no significant effect on the strength of theta modulation in significantly modulated cells.
3.4. Effect of MK-801 on theta modulation of hippocampal single cell discharge Effects of NMDA receptor antagonists on theta rhythm (as well as other biologically significant oscillations) were previously thoroughly characterized (Kiss et al., 2013; Lemercier et al., 2017). In this work we focus on activity of single hippocampal units with respect to the theta rhythm in three recordings with the best quality of LFP signal (33 neurons). Under urethane anesthesia, hippocampal LFP was characterized by periods of theta activity interrupted by periods of slow, low frequency activity (Kiss et al., 2013). To identify the theta intervals, we calculated theta score as the mean FFT power at the range of theta under urethane (3–6 Hz) relative to the mean power at lower (2–3 Hz) and higher (8–10 Hz) frequencies. A theta score higher than 1.3 allowed us to separate the theta state from state with slow activity (Fig. 5A,B). Analysis of LFP signal using FFT showed a peak at ~4 Hz that persisted before and after MK-801 injection (Fig. 5C). Theta rhythm was quantified using modal frequency in theta range, power at theta peak frequency and Q factor (Lemercier et al., 2017; Fig. 5D). None of these parameters were significantly changed after MK-801 injection compared to control recording (Mann-Whitney Rank Sum Test, in all cases U's ≥ 2, p's > 0.4). Modulation of single unit activity by theta rhythm before and after MK-801 injection was characterized during theta intervals (Fig. 5A). MK-801 caused an increase in the proportion of neurons that were significantly modulated by theta rhythm. During baseline conditions, the firing rate of 51.5% neurons was significantly modulated by the theta phase as determined by runs test (Fig. 5G). During the first hour following the MK-801 injection, the number of the neurons that were
4. Discussion 4.1. Summary The neural discoordination hypothesis of schizophrenia postulates that disturbance in temporal organization of neuronal activity leads to disruption in information processing in the brain (Uhlhaas and Singer, 2006; Jones, 2010; Moghaddam and Wood, 2014; Fenton, 2009, 2015). In the context of this hypothesis, we set out to test directly the effect of the psychotomimetic substance dizocilpine (MK-801) on the activity of hippocampal neurons in rats under urethane anesthesia. We found that MK-801 did not affect the firing rate of complex spike cells (putative pyramidal neurons) or theta cells (putative interneurons). MK-801, however, did change the temporal organization of the neuronal 5
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Fig. 5. Effect of MK-801 on the theta modulation of neuronal discharge. (A) Characterizing LFP using time frequency analysis reveals intervals of theta oscillation (~4 Hz) interrupted by intervals of dominant slow oscillations (0.5–4 Hz). Detected theta intervals are marked by the blue band under the time-frequency plot. (Example from a control recording). (B) Examples of two-second intervals of LFP with predominant theta (upper trace) and non-theta (lower trace) from recording in panel A. (C) Power spectra of LFP before and after MK-801 injection. Average power spectra of theta intervals during three recordings are shown. (D) Quantification of effects of MK-801 on theta rhythm using parameters of relative peak power, peak theta frequency, and Q factor. (E) An example of theta phase modulation of a complex spike cell firing before and after MK-801 injection. (F) Polar graphs showing theta phase modulation of neuronal firing for the same cell as in E. Small black arrow indicates average vector length and angle. (G) Percentage of the cells significantly modulated by theta phase. The significance of theta modulation was determined using runs test. (H) Strength of modulation by theta in significantly modulated cells assessed by average length of theta vector. MK-801 had no significant effect on vector length. (I) Theta phase preference in neuronal firing is expressed as average theta vector direction. Data are presented as mean ± SEM. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
coactivated neurons participate in the encoding of the same representations while neurons that do not fire together encode different representations (Harris et al., 2003; Jezek et al., 2011; Kelemen and Fenton, 2010, 2016). In this framework, increased coactivation between originally discoordinated cell pairs caused by the application of MK-801 indicates improper coactivation of distinct representations that should be separate (Fig. 6). Increased correlations between originally anti-correlated cell pairs might reflect easier transitions between different attractor states within hippocampal network. This way, MK-801 may interfere with naturally occurring hippocampal pattern separation processes (Rolls, 2016). Impairment in pattern separation is consistent with the hypothesized instability of spontaneous activity states in schizophrenia, which makes cortical networks prone to noise-induced jumps to a different attractor activation state (Rolls, 2012; Lerner et al., 2012). This finding extends earlier observations of increased similarity between hippocampal ensemble representations of distinct environments after MK-801, assessed by immediate early gene expression (Kubík et al., 2014). The MK-801 injection led to an increase in the number of neurons
discharge that we observed on the level of neuronal pairs, as well as on the level of neuronal multi-unit ensembles. Specifically, MK-801 increased coactivation (coincident firing) between pairs of neurons. This increase was driven by coactivation of originally anti-correlated neuronal pairs, rather than increased coactivation of originally coactive neurons. The changes in neuronal coordination were also detected in the activity of neuronal ensembles. Our observations confirmed the disorganization of neuronal discharge under the influence of psychotomimetic MK-801.
4.2. Increased neuronal coactivation after dizocilpine injection MK-801 injection led to an increase in the number of coactive cell pairs, i.e., cell pairs with a cross-correlation peak at ± 50 ms. The increase in coactivation after injection was mostly a consequence of coactivated firing of cell pairs that were anti-correlated before the injection (Fig. 2E). Coordination of neuronal firing at this timescale is crucial for information processing in the hippocampus (Harris et al., 2003). Consistent with the cell assembly hypothesis (Hebb, 1949), 6
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is toggling between the room and arena representations (Kelemen and Fenton, 2010). Switching between different hippocampal spatial representations is organized by the theta rhythm (Jezek et al., 2011). The oversynchronization of neurons in this task would lead to inadequate separation of the neuronal representations of the two sets of landmarks, which would result in bad behavioral decisions and impaired performance in the task. Impairment in the carousel task was indeed observed after application of MK-801 (Stuchlík and Vales, 2005; Kubík et al., 2014; Svoboda et al., 2015), after application of an alternative NMDA receptor antagonist phencyclidine (Kao et al., 2013), as well as in a neurodevelopmental model of schizophrenia (Lee et al., 2012; O'Reilly et al., 2014). Synaptic plasticity related to memory storage depends on the precise timing of neuronal activation. Discoordination in timing of activity between neurons should thus lead to improper long-term plasticity, and consequently to slower learning. Memory deficits are indeed observed in human patients with schizophrenia (Ranganath et al., 2008). Synaptic plasticity is impaired in rodent models of the disease (Wöhrl et al., 2007; Wiescholleck and Manahan-Vaughan, 2013), and memory deficits were observed in the place avoidance task in rats after MK-801 application (Stuchlík and Vales, 2005; Kubík et al., 2014).
Fig. 6. Hypothetical model of neural coordination deficit after MK-801 administration. (A) Under normal conditions the discharge of hippocampal neurons is organized into coactivated cell assemblies. The illustration depicts a red cell assembly and a blue cell assembly, each active at different cycles of the theta rhythm (middle). Each cell assembly represents a concept – letters A and B in our example (bottom). (B) After MK-801 administration the neural coordination is impaired. Neurons that originally did not discharge at the same time start to discharge together. The cell assemblies that were previously activated separately become disorganized and merged together, and the information that they encoded is lost. Such neuronal coordination deficit can manifest itself behaviorally as impairment in cognitive control. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
4.4. Mechanism of NMDAR antagonist effect on neuronal coordination While the mechanism leading to neural discoordination in our model remains unknown, we can speculate that the effect is mediated by alternation in the activity of GABA-ergic interneurons. NMDA-receptor hypofunction on parvalbumin-expressing inhibitory neurons was suggested as a pathological mechanism in schizophrenia (Jadi et al., 2016), which is consistent with observations of defects in GABA-ergic neurotransmission in the hippocampus, prefrontal and anterior cingulate cortices in human patients with schizophrenia, and in animal models (Benes and Berretta, 2001; Lewis et al., 2005; Homayoun and Moghaddam, 2007; Benes, 2015; Grüter et al., 2015). Alternations in GABA-ergic function lead to excitation-inhibition discoordination (Dragoi and Buzsáki, 2005; Fenton, 2015), which can lead directly to discoordination of neuronal cell assemblies and can also manifest as abnormal neuronal oscillatory activity (Lee et al., 2012, 2014; Kiss et al., 2013; Jadi et al., 2016; Lemercier et al., 2017). Discoordination in activity leads to impaired neuronal communication not only locally, i.e., within a brain structure such as the hippocampus, but also between different brain structures. Discoordination of neuronal activity between CA1 and CA3 sub-regions of the hippocampus after MK-801 administration was observed using immediate early gene expression (Buchtová et al., 2017), and discoordination of activity between two contralateral hippocampi was observed by local field potential measurements (Lee et al., 2012, 2014).
with firing that was significantly modulated by the theta rhythm, while the injection had no effect on theta power itself. Theta rhythm has a strong influence on organizing neuronal discharge in the hippocampus in both awake and urethane anesthetized rats (Fox et al., 1986). The phase of neuronal discharge within theta wave carries behaviorally relevant information (O'Keefe and Recce, 1993, Huxter et al., 2003; Dragoi and Buzsáki, 2006). From the perspective of the neural coordination hypothesis of schizophrenia, the role of theta in organizing activity corresponding to alternative competing representations is particularly interesting (Harris et al., 2003; Jezek et al., 2011). The relevance of theta to pathology in schizophrenia is suggested by decreased theta phase synchrony between the two contralateral hippocampi in a developmental model of the disease (Lee et al., 2012), and is further supported by the fact that compensation of cognitive deficits in the model is accompanied by increased theta synchrony (Lee et al., 2014). In light of these findings, the increased theta modulation of neuronal discharge indicates disrupted coordination between distinct hippocampal representations.
4.3. From neuronal oversynchronisation to cognitive discoordination 5. Conclusions The disorganized neuronal discharge identified in our study may lead to a cognitive coordination deficit, which is prominent among the cognitive symptoms of schizophrenia and psychosis. Cognitive coordination is manifested as the ability to distinguish between relevant and irrelevant information, and the ability to use the former and ignore the latter in response to actual behavioral situations. The effect of psychotomimetic manipulations on cognitive coordination has been studied in animal models using a place avoidance task on a rotating carousel (Stuchlik et al., 2004; Lee et al., 2012). In this task, the rat is exposed to two sets of environmental landmarks – stationary room landmarks and rotating arena landmarks, and must use the relevant set and ignore the irrelevant one according to behavioral circumstances. Under these conditions, the hippocampal neurons that are simultaneously active at any particular moment tend to represent the same set of landmarks (either arena, or room) and the ensemble activation state
The inadequate treatment options available for schizophrenia, especially its cognitive symptoms, and the slow progress in development of new treatment strategies (Lieberman et al., 2005; Abbott, 2010) emphasize the importance of understanding the neuronal mechanisms of the disorder. We tested the discoordination hypothesis of psychosis by recording activity of hippocampal neuronal ensembles after administration of psychotomimetic MK-801. MK-801 led to increased coactivation of firing between neurons. The increase was primarily driven by increases in coincident firing between previously anti-correlated cells. Increased pairwise coactivation manifested as disorganized discharge on the level of neuronal ensembles, which in turn could lead to disorganization in information processing. Disorganization of neuronal activity after a psychotomimetic dose of MK-801 supports the discoordination hypothesis of psychosis. 7
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Grant support This research was mainly funded by the Czech Science Foundation grant 17-04047S. Institutional support for Institute of Physiology, Czech Academy of Sciences was provided by RVO: 67985823. Institutional support for NIMH was provided by the project “Sustainability for the National Institute of Mental Health”, under grant number LO1611, with financial support from the Ministry of Education, Youth and Sports of the Czech Republic, under the NPU I program. Partly supported by ERDF project, OPPK Mikroskopický systém. Ethical statement All experimental procedures were conducted in accordance with the Animal Protection Code of the Czech Republic and the corresponding directive of the European Community Council on the use of laboratory animals (2010/63/EC). Authors have no conflict of interests. Acknowledgements We thank Daniel Klement and David Levčík for their contribution to the early practical and theoretical development of this study, to Ehud Kaplan and Rachel R. Horsley for useful comments on a previous version of the manuscript and to Rachel R. Horsley also for proofreading the manuscript. References Abbott, A., 2010. The drug deadlock. Nature 468, 158–159. http://dx.doi.org/10.1038/ 468158a. Adler, C.M., Goldberg, T.E., Malhotra, A.K., Pickar, D., Breier, A., 1998. Effects of ketamine on thought disorder, working memory, and semantic memory in healthy volunteers. Biol. Psychiatry 43 (11), 811–816. Adler, C.M., Malhotra, A.K., Elman, I., Goldberg, T.E., Egan, M., Pickar, D., Breier, A., 1999. Comparison of ketamine-induced thought disorder in healthy volunteers and thought disorder in schizophrenia. Am. J. Psychiatry 156, 1646–1649. Benes, F.M., 2015. Building models for postmortem abnormalities in hippocampus of schizophrenics. Schizophr. Res. 167, 73–83. http://dx.doi.org/10.1016/j.schres. 2015.01.014. Benes, F.M., Berretta, S., 2001. GABAergic interneurons: implications for understanding schizophrenia and bipolar disorder. Neuropsychopharmacology 25, 1–27. Buchtová, H., Fajnerová, I., Stuchlík, A., Kubík, Š., 2017. Acute systemic MK-801 induced functional uncoupling between hippocampal areas CA3 and CA1 with distant effect in the retrosplenial cortex. Hippocampus 27, 134–144. http://dx.doi.org/10.1002/hipo. 22678. Cimadevilla, J.M., Fenton, A.A., Bures, J., 2000. Functional inactivation of dorsal hippocampus impairs active place avoidance in rats. Neurosci. Lett. 285 (1), 53–56. Dragoi, G., Buzsáki, G., 2005. Cortical inhibitory neurons and schizophrenia. Nat. Rev. Neurosci. 6 (4), 312–324. Dragoi, G., Buzsáki, G., 2006. Temporal encoding of place sequences by hippocampal cell assemblies. Neuron 50 (1), 145–157. Engel, A.K., König, P., Singer, W., 1991. Direct physiological evidence for scene segmentation by temporal coding. Proc. Natl. Acad. Sci. U. S. A. 88 (20), 9136–9140. Fenton, A.A., 2009. Neural coordination and psychotic disorganization. In: Holscher, C., Munk, M.H. (Eds.), Information Processing by Neuronal Populations. Cambridge University Press, London, pp. 387–408. Fenton, A.A., 2015. Excitation-inhibition discoordination in rodent models of mental disorders. Biol. Psychiatry 77, 1079–1088. Fenton, A.A., Lytton, W.W., Barry, J.M., Lenck-Santini, P.P., Zinyuk, L.E., Kubík, S., Bures, J., Poucet, B., Muller, R.U., Olypher, A.V., 2010. Attention-like modulation of hippocampus place cell discharge. J. Neurosci. 30 (13), 4613–4625. http://dx.doi.org/ 10.1523/JNEUROSCI.5576-09.2010. Fox, S.E., Wolfson, S., Ranck Jr., J.B., 1986. Hippocampal theta rhythm and the firing of neurons in walking and urethane anesthetized rats. Exp. Brain Res. 62 (3), 495–508. Grüter, T., Wiescholleck, V., Dubovyk, V., Aliane, V., Manahan-Vaughan, D., 2015. Altered neuronal excitability underlies impaired hippocampal function in an animal model of psychosis. Front. Behav. Neurosci. 9, 117. http://dx.doi.org/10.3389/ fnbeh.2015.00117. Hara, K., Harris, R.A., 2002. The anesthetic mechanism of urethane: the effects on neurotransmitter-gated ion channels. Anesth. Analg. 94 (2), 313–318. Harris, K.D., Csicsvari, J., Hirase, H., Dragoi, G., Buzsáki, G., 2003. Organization of cell assemblies in the hippocampus. Nature 424 (6948), 552–556. Hebb, D., 1949. The Organization of Behavior. Wiley & Sons, New York. Homayoun, H., Moghaddam, B., 2007. NMDA receptor hypofunction produces opposite effects on prefrontal cortex interneurons and pyramidal neurons. J. Neurosci. 27 (43), 11496–11500. Huxter, J., Burgess, N., O'Keefe, J., 2003. Independent rate and temporal coding in
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