A network model of the modulation of gamma oscillations by NMDA receptors in cerebral cortex

Schizophrenia is described as a disorder with positive, negative and cognitive symptoms, and previous work has identified alterations in neurotransmitter systems, anatomy and neural rhythms in affected patients. In particular, many studies report increased power and/or phase synchrony of gamma-band oscillations in early-course patients, and higher gamma activity correlates with greater psychotic symptom load. Sub‑anaesthetic doses of NMDA receptor (NMDAR) antagonists such as ketamine produce a transient psychotic state in humans and animals that resembles schizophrenia and also boost gamma power, but the circuit mechanisms linking NMDAR hypofunction, increased gamma and altered perception remain uncertain. Susin and colleagues set out to explore how partial blockade of NMDARs alters cortical network dynamics and responsiveness, using a biophysically informed computational network model. The study aims to reproduce experimental features induced by NMDAR antagonists, test the hypothesis that preferential blockade of NMDARs on inhibitory interneurons can account for paradoxical network excitation, and to examine whether the enhanced gamma accompanying NMDAR block is responsible for changes in network responsiveness to external inputs.

The investigators implemented a spiking network model composed of two cell types: 4,000 Regular Spiking (RS) excitatory neurons and 1,000 Fast Spiking (FS) inhibitory neurons (total N = 5,000). Neurons were modelled with the Adaptive Exponential Integrate-and-Fire (AdEx) formalism, which includes spike-triggered and subthreshold adaptation terms. Connectivity was random with a connection probability of 10%, yielding on average 500 excitatory and 100 inhibitory presynaptic inputs per neuron. Synaptic currents comprised AMPA and NMDA excitatory components and GABAA inhibitory currents. AMPA and GABAA conductances were implemented as fast exponential decays (τAMPA decay = 1.5 ms, τGABAA decay = 7.5 ms), while NMDA conductances used a biexponential kernel (τNMDA rise = 2 ms, τNMDA decay = 200 ms) and included a voltage-dependent magnesium block term. Synaptic strengths for AMPA and GABAA were set to values taken from prior work (QAMPA = 5 nS, QGABAA = 3.34 nS) and all synapses had a 1.5 ms delay. The NMDA/AMPA charge ratio was on average larger in RS than in FS cells, consistent with experimental reports. External drive to mimic cortical input consisted of Next = 5,000 independent excitatory Poisson spike trains connected with 10% probability and QAMPA Ext = 0.8 nS. Two baseline drive regimes were used: µext = 3 Hz to elicit gamma activity and µext = 2 Hz to produce asynchronous‑irregular (AI) states. To probe responsiveness, the authors added a time‑varying Gaussian-shaped increase in the external Poisson rate (standard deviation 50 ms) of variable amplitude, applied to both AMPA and NMDA external inputs. NMDAR antagonism was modelled by reducing the NMDA synaptic weights QNMDA onto RS and FS populations along trajectories in the (QNMDA RS, QNMDA FS) parameter space intended to mimic progressive channel block. Simulations were run in the Brian2 simulator with Euler integration (dt = 0.1 ms). Population activity was summarised via a simulated local field potential (LFP) obtained by convolving spike trains with an experimentally derived unitary LFP kernel; for spectral analyses the Welch method was used (Hamming window 0.25 s, 125 overlapping points). Responsiveness R was defined as the spike-count difference between stimulated and unstimulated conditions, normalised by network size and time window.

Simulations reproduced several empirical features associated with sub‑anaesthetic NMDAR antagonists. As NMDA synaptic strengths were decreased, the model showed an increase in the firing rate of RS excitatory neurons and a decrease in firing of FS inhibitory neurons. Concomitantly, the population-level gamma-band power increased under partial NMDA blockade. The authors obtained these effects when the NMDAR block affected interneurons preferentially; parameter sets with larger decreases of QNMDA onto FS cells produced network excitation and boosted gamma power that match experimental observations. The study distinguished two dynamical quantities: excitability (overall spontaneous spiking) and responsiveness (additional spikes elicited by a transient external stimulus). Across a range of stimulus amplitudes, responsiveness of RS cells increased as NMDA blockade deepened, whereas responsiveness of FS cells decreased. Thus, in the model both excitability and RS responsiveness tended to increase together under interneuron‑biased NMDAR block. Comparisons between gamma states and asynchronous‑irregular (AI) states showed that AI states consistently yielded higher responsiveness than gamma states for the same stimulus protocol. Importantly, the increase in responsiveness associated with NMDAR antagonism was not specific to gamma oscillatory regimes: enhanced responsiveness was observed both during pronounced gamma rhythms and during AI regimes lacking apparent gamma. Mechanistically, the authors attribute the responsiveness change to oppositely directed membrane shifts: RS cells became depolarised under NMDAR antagonism while FS cells were relatively hyperpolarised, producing greater stimulus‑evoked output from excitatory cells. The model also reports that the NMDA/AMPA charge balance is higher in RS neurons than in FS neurons, a feature used in constructing the parameter sets.

Susin and colleagues interpret their results as supporting a parsimonious circuit mechanism linking NMDAR hypofunction to increased gamma and to abnormal sensory responses. They highlight three principal findings: (1) modulation of gamma rhythms by partial NMDAR block in a two‑population PING (pyramidal‑interneuron gamma) network is reproduced when antagonism predominantly affects interneuronal NMDARs; (2) partial NMDAR blockade is accompanied by increased network responsiveness to external inputs; and (3) the responsiveness increase is not contingent on the presence of gamma oscillations, because asynchronous states showed similar or even greater responsiveness. The authors place these findings in the context of experimental literature that reports interneuron vulnerability to NMDAR antagonists and ketamine‑induced disinhibition of glutamatergic neurons. Their model reproduces experimental observations—including increased glutamatergic activity and enhanced gamma power—only when the NMDA synaptic strengths on FS interneurons are decreased more than on RS cells, which they argue supports the hypothesis that hypofunction in parvalbumin‑positive fast spiking interneurons is important for schizophrenia‑like phenotypes. At the same time the paper acknowledges contrasting experimental reports that question the relative impact of NMDAR block on excitatory versus inhibitory neurons, and notes that the precise cellular locus of NMDAR hypofunction in schizophrenia remains unresolved. Regarding clinical and mechanistic implications, the investigators propose that the increased responsiveness of cortical networks under NMDAR antagonism could underlie exacerbated sensory responses and phenomena such as altered perception or hallucinations observed with ketamine and in psychosis. They suggest empirical tests of the model predictions, for instance comparing stimulus‑evoked responses in cortical slices or in vivo before and after application of NMDAR antagonists. The authors are careful to report that some experimental results are conflicting and that their model assumptions—most notably the interneuron‑biased NMDAR block—are critical for reproducing the observed phenomena.