Abstract

Excitation–inhibition balance (E/I balance) is a fundamental property of cortical microcircuitry. Disruption of E/I balance in prefrontal cortex is hypothesized to underlie cognitive deficits observed in neuropsychiatric illnesses such as schizophrenia. To elucidate the link between these phenomena, we incorporated synaptic disinhibition, via N-methyl-D-aspartate receptor perturbation on interneurons, into a network model of spatial working memory (WM). At the neural level, disinhibition broadens the tuning of WM-related, stimulus-selective persistent activity patterns. The model predicts that this change at the neural level leads to 2 primary behavioral deficits: 1) increased behavioral variability that degrades the precision of stored information and 2) decreased ability to filter out distractors during WM maintenance. We specifically tested the main model prediction, broadened WM representation under disinhibition, using behavioral data from human subjects performing a spatial WM task combined with ketamine infusion, a pharmacological model of schizophrenia hypothesized to induce disinhibition. Ketamine increased errors in a pattern predicted by the model. Finally, as proof-of-principle, we demonstrate that WM deteriorations in the model can be ameliorated by compensations that restore E/I balance. Our findings identify specific ways by which cortical disinhibition affects WM, suggesting new experimental designs for probing the brain mechanisms of WM deficits in schizophrenia.

Introduction

A basic principle of cortical computation is the dynamically balanced interaction of excitatory pyramidal cells and inhibitory interneurons (Shadlen and Newsome 1994; van Vreeswijk and Sompolinsky 1996; Shu et al. 2003). Disruption of the ratio of excitation and inhibition (E/I balance) can give rise to profound behavioral deficits and may play a key role in serious mental illnesses such as schizophrenia (Yizhar et al. 2011). Specifically, there is growing support for a hypothesis that cortical disinhibition occurs in schizophrenia, due to the disrupted functioning of inhibitory interneurons, which results in elevated E/I ratio (Lewis et al. 2005; Marin 2012). However, it remains unclear how disinhibition may produce deficits in higher cognition, which are a prominent feature of neuropsychiatric diseases.

Cognitive deficits are postulated to be at the “core” of schizophrenia neuropathology (Elvevåg and Goldberg 2000; Barch and Ceaser 2012), with a severe deficit in working memory (WM), the ability to transiently maintain and manipulate information internally (Goldman-Rakic 1994; Lee and Park 2005). In addition to maintenance over time, robust WM requires shielding internal representations from interference by both internal noise and external distraction. This “filtering” function, in particular, may be severely compromised in schizophrenia (Anticevic et al. 2011).

The prefrontal cortex is a key node in the distributed cortical network recruited during WM (Owen et al. 2005; Fuster 2008) and exhibits altered inhibitory microcircuitry in schizophrenia (Lewis et al. 2005; Bitanihirwe et al. 2009). Primate electrophysiological studies show that persistent firing of prefrontal pyramidal cells supports robust and stable WM representations (Funahashi et al. 1989) that depend critically on E/I balance (Rao et al. 2000). Biophysically realistic computational models have delineated a neural circuit basis of WM activity through 2 key network properties: Strong recurrent excitation to sustain persistent activity and recruitment of lateral inhibition to shape selectivity of representations (Compte et al. 2000; Brunel and Wang 2001).

An open question is whether cortical disinhibition, specifically within a WM microcircuit, can give rise to the types of deficits that may be observed in schizophrenia. To test this hypothesis, we examined the neural and behavioral effects of disinhibition in a spiking neural network model of spatial WM (Compte et al. 2000). Specifically, we induce disinhibition in the model via perturbation of N-methyl-D-aspartate receptors (NMDARs) on inhibitory interneurons, which weakens the recruitment of feedback inhibition. This mechanism may be linked to the pathophysiology of schizophrenia (Lisman et al. 2008; Nakazawa et al. 2012) and possibly accounts for some of the key effects of NMDAR antagonists, such as ketamine, which are a leading pharmacological model of schizophrenia (Krystal et al. 1994).

The model predicts that, at the neural level, disinhibition elevates baseline firing rates and broadens WM activity patterns. At the behavioral level, disinhibition increases deterioration over time of the precision of stored information during WM and increases the range of distractors that can interfere with WM. To experimentally investigate synaptic mechanisms and test the main model prediction of broadened WM representations under disinhibition, we used behavioral data from healthy human subjects performing a spatial WM match/nonmatch task following ketamine administration. It was found that performance errors increased with ketamine specifically for those nontarget test probes that are similar to the remembered targets, as predicted by the model. Finally, as proof-of-principle, we demonstrate that synaptic compensations that restore E/I balance in the model can ameliorate the effects of disinhibition.

Materials and Methods

We used an extensively validated spiking network model of spatial WM, consisting of pyramidal cells and interneurons, structured in a ring architecture (Fig. 1A; Compte et al. 2000; Carter and Wang 2007). Parameters were modified starting from the original “modulated parameter set” of Compte et al. (2000) to produce 1) a narrower persistent activity pattern (adjusting the connectivity profile), 2) drift more comparable with experimental observations during human behavior (Badcock et al. 2008; adjusting background input), and 3) robustness of multistability to small (∼1%) decreases in recurrent excitatory conductances (adjusting recurrent conductances). Notably, all reported effects were present in the original parameter set of Compte et al. (2000) and were quite robust to parameter modulations.

Figure 1.

Recurrent model of spatial WM and disinhibition mechanism. (A) Schematic network architecture. The model consists of recurrently connected excitatory pyramidal cells (E) and inhibitory interneurons (I). Pyramidal cells are labeled by the angular location they encode (0–360°). Excitatory-to-excitatory connections are structured, such that neurons with similar preferred angles are more strongly connected. Connections between pyramidal cells and interneurons are unstructured and mediate feedback inhibition. (B) Disinhibition mechanism. NMDAR hypofunction on interneurons (1; decreased NMDAR conductance on interneurons, GEI) weakens the recruitment of feedback inhibition. As a result, pyramidal cells are disinhibited and exhibit increased firing rates (2; increased firing rate of pyramidal cells, rE).

Figure 1.

Recurrent model of spatial WM and disinhibition mechanism. (A) Schematic network architecture. The model consists of recurrently connected excitatory pyramidal cells (E) and inhibitory interneurons (I). Pyramidal cells are labeled by the angular location they encode (0–360°). Excitatory-to-excitatory connections are structured, such that neurons with similar preferred angles are more strongly connected. Connections between pyramidal cells and interneurons are unstructured and mediate feedback inhibition. (B) Disinhibition mechanism. NMDAR hypofunction on interneurons (1; decreased NMDAR conductance on interneurons, GEI) weakens the recruitment of feedback inhibition. As a result, pyramidal cells are disinhibited and exhibit increased firing rates (2; increased firing rate of pyramidal cells, rE).

Single-Neuron Models

Pyramidal cells and interneurons are modeled as leaky integrate-and-fire neurons (Tuckwell 1988), characterized by total capacitance Cm, leak conductance gL, resting potential VL, spike threshold potential Vth, reset potential Vres, and refractory time τref. For pyramidal cells, Cm = 0.5 nF, gL = 25 nS, VL = −70 mV, Vth = −50 mV, Vres = −60 mV, and τref = 2 ms; for interneurons, Cm = 0.2 nF, gL = 20 nS, VL = –70 mV, Vth = –50 mV, Vres = –60 mV, and τref = 1 ms. The subthreshold membrane potential, V (t), follows:

(1)
$$C_{\rm m} \displaystyle{{{\rm d}V(t)} \over {{\rm d}t}} = - g_{\rm L} (V(t) - V_{\rm L} ) - I(t),$$
where I(t) is the total input current to the cell.

Synaptic Interactions

The network consists of NE = 2048 pyramidal cells and NI = 512 inhibitory interneurons. Neurons receive recurrent, background, and external inputs. Excitatory synaptic currents are mediated by 2-amino-3-(3-hydroxy-5-methyl-isoxazol-4-yl)propanoic acid [AMPA] receptors (AMPARs) and NMDARs, and inhibitory synaptic currents are mediated by γ-aminobutyric acid type A (GABAA) receptors (GABARs). The total input current to each neuron is:

(2)
$$I = I_{{\rm NMDA}} + I_{{\rm AMPA}} + I_{{\rm GABA}} + I_{{\rm ext}} ,$$
where Iext delivers stimulus input to pyramidal cells. The dynamics of synaptic currents for neuron i follow:
(3)
$$I_{i,{\rm AMPA}} = (V_i - V_{\rm E} )\sum\limits_j {g_{\,ji,{\rm AMPA}} s_{\,j,{\rm AMPA}} } ,$$

(4)
$$I_{i,{\rm NMDA}} = (V_i - V_{\rm E} )\displaystyle{{\sum\nolimits_j {g_{\,ji,{\rm NMDA}} s_{\,j,{\rm NMDA}} } } \over {1 + [{\rm Mg}^{{\rm 2 + }} {\rm ]}{\rm\, exp}( - 0.062\;V_i /3.57\;{\rm mV})}},$$

(5)
$$I_{i,{\rm GABA}} = (V_i - V_{\rm I} )\sum\limits_j {g_{\,ji,{\rm GABA}} s_{\,j,{\rm GABA}} } ,$$
where VE = 0 mV and VI = –70 mV, and $$g_{\,ji,{\rm syn}}$$ denotes the synaptic conductance strength on neuron i from neuron j. NMDAR-mediated currents exhibit voltage dependence controlled by the extracellular magnesium concentration [Mg2+] = 1 mM (Jahr and Stevens 1990).

Given a spike train {tk} in the presynaptic neuron j, the gating variables sj,AMPA and sj,GABA for AMPAR- and GABAR-mediated currents, respectively, are modeled as:

(6)
$$\displaystyle{{{\rm d}s} \over {{\rm d}t}} = - \displaystyle{s \over {\tau _s }} + \sum\limits_k {\delta (t - t_k )} .$$

The gating variable sj,NMDA for NMDAR-mediated current is modeled as:

(7)
$$\displaystyle{{{\rm d}x} \over {{\rm d}t}} = - \displaystyle{x \over {\tau _x }} + \sum\limits_k {\delta (t - t_k )} ,$$

(8)
$$\displaystyle{{{\rm d}s} \over {{\rm d}t}} = - \displaystyle{s \over {\tau _s }} + \alpha _s x(1 - s),$$
with αs = 0.5 kHz and τx = 2 ms. The decay time constant τs is 2 ms for AMPA, 10 ms for GABA, and 100 ms for NMDA. For simplicity, background inputs are mediated entirely by AMPARs, and recurrent excitatory inputs are mediated entirely by NMDARs, as they are critical for the stability of persistent activity (Wang 1999; Compte et al. 2000). Background input to each neuron is provided by an independent Poisson spike train with the rate of 0.6 kHz and AMPAR conductances of 9.3 nS on pyramidal cells and 7.14 nS on interneurons.

Network Model

As noted, pyramidal cells are organized in a ring architecture and are tuned to the angular location on a circle (0–360°), with uniform distribution of their preferred angles. The network structure follows a columnar architecture, such that pyramidal cells with similar stimulus selectivity are preferentially connected to each other. The synaptic conductance on neuron i from neuron j, $$g_{\,ji,{\rm syn}} = W(\theta _j - \theta _i )G_{{\rm syn}}$$, where θi is the preferred angle of neuron i, and $$W(\theta _j - \theta _i )$$ is the connectivity profile normalized such that:

(9)
$${\textstyle{1 \over {360^\circ }}}\int_{0^\circ }^{360^\circ } {W(\theta ){\rm d}\theta = 1} .$$

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