Blocking NMDAR Disrupts Spike Timing and Decouples Monkey Prefrontal Circuits: Implications for Activity-Dependent Disconnection in Schizophrenia

Abstract

We employed multi-electrode array recording to evaluate the influence of NMDA receptors (NMDAR) on spike-timing dynamics in prefrontal networks of monkeys as they performed a cognitive control task measuring specific deficits in schizophrenia. Systemic, periodic administration of an NMDAR antagonist (phencyclidine) reduced the prevalence and strength of synchronous (0-lag) spike correlation in simultaneously recorded neuron pairs. We employed transfer entropy analysis to measure effective connectivity between prefrontal neurons at lags consistent with monosynaptic interactions and found that effective connectivity was persistently reduced following exposure to the NMDAR antagonist. These results suggest that a disruption of spike timing and effective connectivity might be interrelated factors in pathogenesis, supporting an activity-dependent disconnection theory of schizophrenia. In this theory, disruption of NMDAR synaptic function leads to dysregulated timing of action potentials in prefrontal networks, accelerating synaptic disconnection through a spike-timing-dependent mechanism.

Keywords: NMDAR antagonist; STDP; animal model; cross correlation; effective connectivity; neural synchrony; nonhuman primate; spike correlation; transfer entropy.

Figure 1.. DPX Task, Recording Locations, and Task Performance

(A and B) Event sequence in the DPX task. A cue and a probe stimulus are displayed each trial. (A) On AX trials, the A-cue is followed by the X-probe. This is the target sequence, and the monkey is rewarded for moving the joystick to the left (target response) following probe onset. (B) On BX trials, the B-cue stored in working memory must override the habitual target response to the X-probe to produce the correct nontarget response to the X-probe in this context. (C) Cue stimuli. One dot pattern was designated the A-cue, and five dot patterns were collectively designated B-cues. (D) Probe stimuli. One dot pattern was designated the X-probe, and five dot patterns were collectively designated Y-probes. (E and F) Locations of ensemble neural recording (cyan) in Brodmann area 46 (tan) surrounding the principal sulcus (PS) in monkeys 1 (E) and 2 (F). Red dots indicate the centers of the recording chambers. (G) Behavioral performance on the DPX task while the neural data described in this report was recorded. The y axis indicates the proportion of errors (±2 SE above and below the mean) committed during DPX task performance separated by cue-probe sequence (trial type) and experimental condition in monkeys 1 (solid) and 2 (dashed).

Figure 2.. Influence of NMDAR Antagonist on Spike Synchrony in Prefrontal Cortex

Results of cross-correlation analysis applied to the spike trains of pairs of simultaneously recorded prefrontal neurons. Experimental condition is indicated by color (B-E): drug-naive (black), saline (blue), and drug (red). (A) Example CCH of a neuron pair recorded in the drug-naive condition with a significant CCH peak at 0-lag. The CCH is normalized by dividing counts in each bin in the original data by the mean count of the bootstrap distribution of spike-jittered CCHs at that bin. (B) Proportion of all neuron pairs recorded that exhibited a significant peak at 0-lag (±1 ms). A neuron pair was considered to exhibit a significant 0-lag CCH peak if the sum of the counts of joint spikes at lags − 1,0, and +1 exceeded the 99^th percentile of the distribution of corresponding sums computed from the spike-jittered CCHs. p values reflect significance of differences in proportion of significantly coupled neuron pairs across experimental conditions (Fisher’s exact test; n = 1,736/14,674, 469/4,908, and 518/12,112 significant pairs out of total for naive, saline, and drug, respectively). (C) Mean population CCHs separated by experimental condition (insert shows the 0-lag peak of the same data on an expanded ±3 ms lag timescale). All recorded neuron pairs were included. CCH normalization as in (A). (D) Median 0-lag CCH peak height as a function of experimental condition. For each neuron pair, 0-lag peak height was computed as the sum of joint spike counts in the −1, 0, and +1 lag bins of the original data and was normalized by dividing by the mean of the sum of counts in the same bins of the spike-jittered CCHs. Data plotted reflects the median of the distribution of normalized 0-lag peak heights across all recorded neuron pairs in each experimental condition (error bars reflect 95% confidence intervals of the median; p values calculated by Kruskal-Wallis test followed by Tukey’s HSD test). (E) Cumulative distributions of mean firing rate by experimental condition. No significant differences in mean firing rate were found (Kruskal-Wallis test; saline versus drug, p = 0.99; naive versus drug, p = 0.90, saline versus drug p = 0.97).

Figure 3.. Influence of NMDAR Antagonists on Spike Timing Variability in Prefrontal Cortex

To quantify variability in spike timing we computed the Fano factor as the variance in spike counts over a sequence of 50-ms bins divided by the mean firing rate on a per-neuron basis. (A and B) Frequency distribution of individual neuron Fano factor values for drug and saline (A), and drug and drug-naive (B) conditions. Vertical dashed lines in (A) and (B) indicate a Fano factor of 1, triangles represent the median of each distribution. (C) Median Fano factor (error bars indicate 95% confidence intervals) by experimental condition; p values calculated by Kruskall-Wallis test followed by Tukey’s HSD test.

Figure 4.. Spike Correlation at 0-Lag Was Strongly Modulated in Time throughout the Trial

Heatmaps plot the population average spike correlation between pairs of simultaneously recorded prefrontal neurons as a function of lag between spikes in the two neurons (vertical axis; 1-ms resolution) and time within the DPX trial (horizontal axis; 50-ms resolution). Data in all panels were restricted to AX trials. Spike times were aligned to cue onset and data from all simultaneously recorded pairs of neurons were included. (A–C) Average population spike correlation in the drug-naive (A), saline (B), and drug (C) conditions. Spike density functions (white lines) superimposed on each panel illustrate concurrent modulation in population firing rate (firing rate scales at right). Arrows under each panel indicate mean reaction time (RT) in each experimental condition. (D) Population average spike correlation averaged across time in the trial at spike lags from −20 to +20 ms and plotted separately by experimental condition. Horizontal lines indicate the heights of the 0-lag peaks of corresponding color. (E) Same data as in (D) but plotted on an expanded y axis to better illustrate spike correlation at flanking non-zero lags. (F) Population average spike correlation computed using trial-shuffled spike trains from the same neuronal pairs as in (D). (G) Population average spike density functions illustrate modulation in population firing rate of the same neurons contributing to the correlation analysis throughout the DPX trial plotted separately by experimental condition. (H) Time course of population average 0-lag spike correlation in pairs of prefrontal neurons using spike train data aligned to the time of the motor response (vertical dashed line).

Figure 5.. Transfer Entropy in Pairs of Simultaneously Recorded Prefrontal Neurons

(A) Example bias-corrected transfer entropy (TE) function for a pair of prefrontal neurons recorded in the drug-Naive condition. For each neuron pair, TE values (black bars) in each lag bin were bias corrected by subtracting the mean at the corresponding lag bin of a bootstrap distribution of TE values obtained from the same neurons after randomly jittering all spike times within a± 30 ms window to destroy the temporal relationship between the spike trains (gray lines plot TE functions computed from spike jittered data). (B) The proportion of all neuronal pairs recorded that were identified as significantly coupled by TE analysis separated by experimental condition. Neuron pairs were considered significantly coupled if the peak of the TE time course exceeded the 99.9^th percentile of peaks in any lag bin in 1000 spike-jittered bootstrap iterations of the analysis. Neuron pairs (coupled/all): drug-naive: 677/14,468 pairs; saline: 150/4,914 pairs; drug: 365/12,230 pairs. p values reflecting the significance of differences between experimental conditions were calculated using Fisher’s exact test. (C) Distribution of time bins (lags) in which peak TE values occurred, across all neuron pairs. Dashed line indicates the expected values if peak locations were uniformly distributed across lags. (D) Median bias-corrected peak TE values across all neuron pairs (error bars reflect 95% confidence intervals of the median); p values reflecting the significance of differences between experimental conditions were calculated using the Kruskall-Wallis test followed by Tukey’s HSD test.

Figure 6.. Periodic Exposure to NMDAR Antagonists Is Associated with Persistent Reduction in Both 0-Lag Spike Synchrony and Lagged Functional Interactions in Prefrontal Networks

Comparison of 0-lag CCH and lagged TE functional coupling metrics between days when no injection was given before first drug exposure (“No Inject.,” gray), days when saline was injected before first drug exposure (“Saline before drug,” dark blue), days when saline was injected after first drug exposure (“Saline after drug,” light blue), and days that PCP was injected (“Drug,” red). “No inject.” and “Saline before drug” data together comprise the “Naive” experimental condition in Figures 1, 2, 3, 4, and 5. p values reflecting the significance of differences between experimental conditions were calculated using the Kruskal-Wallis test followed by Tukey’s HSD test (“n.s.” indicates p > 0.05, not significant). (A) Median normalized 0-lag CCH peak height as a function of experimental condition (data restricted to pairs with significant 0-lag peaks; error bars reflect 95% confidence intervals of the median). Number of neuron pairs: “No injection” = 1,099; “Saline before drug” = 637; “Saline after drug” = 469; “Drug” = 518. (B) Median bias-corrected nonzero lag TE peak height as a function of experimental condition. (All neuronal pairs included in the TE analysis to increase the power to detect differences between experimental conditions). Number of neuron pairs: “No injection” = 6,762; “Saline before drug” = 7,706; “Saline after drug” = 4,914; “Drug” = 12,230.

Figure 7.. Patterns of Functional Coupling between Neurons Reflect the Physiological Signals They Generate during Behavior

(A and B) Average population activity of class I (A) and class II (B) neurons defined by their patterns of task-related activity during the DPX task. (See STAR Methods for statistical criteria defining class I and II neurons.) Population spike density functions of different colors illustrate firing rate over time on subsets of trials separated by cue-probe sequence (AX, AY, BX, and BY). (A) Class I (39% of recorded neurons): on B-cue trials, class I neurons exhibit a strong response to the cue. On A-cue trials, class I neurons exhibit a ramping of activity that peaks in the probe period. (B) Class II (14% of recorded neurons): neurons in class II do not exhibit a strong response to the cue (differentiating them from class I neurons). Rather they exhibit a ramping of activity during the probe period that is greater on B-cue than A-cue trials. (C and D) Relation between neuronal pair type and functional coupling. Neuron pairs in this analysis were restricted to those in which both neurons exhibited task-related activity and were assigned either to class I or II. “Like pairs” were defined as those in which the two neurons belonged to the same physiological class (either both neurons in class I or II). “Unlike pairs” were defined as those in which one neuron belonged to class I and the other to class II. Bars indicate the percent of all recorded like neuron pairs (black) and unlike neuron pairs (gray) that were significantly coupled either (C) at 0-lag (CCH) or (D) at nonzero lag (TE). p values indicate the significance of the difference in proportions (Fisher’s exact test). (C) Percent of recorded like pairs (black; n = 3,324) and unlike pairs (gray; n = 1,729) that were significantly coupled at 0-lag by CCH analysis. (D) Percent of recorded like pairs (black; n = 3,424) and unlike pairs (gray; n = 1,757) that were significantly coupled at nonzero lag by TE analysis.

Figure 8.. Spike-Timing-Dependent Synaptic Disconnection in Schizophrenia

In this theory, multiple insults that increase risk for schizophrenia converge on a common effect that is to reduce synchronous and near-synchronous spiking in cortical networks, preferentially but not exclusively impacting prefrontal cortical networks. These insults include: (1) SNPs identified in GWAS studies that are likely to modify the function or expression of synaptic proteins that mediate or modulate NMDAR synaptic transmission, (2) developmental events that include normal synaptic pruning in adolescence, which may be accelerated by risk mutations in schizophrenia, and (3) environmental factors, such as stress, that might influence oscillatory rhythms in cortical networks or other aspects of neural system dynamics that influence spike timing. The reduction in synchronous spiking downstream of these insults then drives synaptic disconnection of prefrontal cortical networks via spike-timing plasticity mechanisms. Clinical symptoms and cognitive deficits seen in schizophrenia emerge largely as a consequence of the eventual disconnection of prefrontal cortical networks. Loss of synaptic connectivity contributes to the progressive loss of cortical gray matter volume, particularly affecting prefrontal cortex.