Linking microcircuit dysfunction to cognitive impairment: effects of disinhibition associated with schizophrenia in a cortical working memory model

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.

Keywords: NMDAR hypofunction; disinhibition; prefrontal cortex; schizophrenia; working memory.

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, G_EI) 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, r_E).

Figure 2.

Disinhibition broadens WM representation. (A) The spatiotemporal plot of persistent activity patterns for control (upper panel) and disinhibition (3.25% reduction of G_EI) condition (lower panel). A stimulus is presented at 90° for 250 ms and, during the subsequent delay, the stimulus location is encoded by persistent activity of a WM bump. Disinhibition broadens the network activity pattern. (B) The firing rate profile of the WM bump for control and disinhibition conditions averaged over 500 ms in a single trial. (C) Increase of bump width as a function of the degree of disinhibition. (D) Increase of baseline firing rates as a function of the degree of disinhibition for both pyramidal cells and interneurons, from their control values of 1 and 6.4 Hz, respectively. Error bars show standard error of the mean.

Figure 3.

The parameter space of NMDAR hypofunction highlights the importance of E/I balance for multistability and bump width. Bump width increases with the reduction of G_EI and decreases with the reduction of G_EE. Bump width can be maintained with a proportional decrease in both G_EI and G_EE. The colored diagonal band is the region with multistability, in which the network supports both a low-rate symmetric baseline state and a family of WM bump states. In the white region at the bottom right corner, the baseline state is destabilized, due to strong disinhibition. In the black region at the top left corner, the bump state is destabilized, due to insufficient recurrent excitation.

Figure 4.

Disinhibition degrades memory precision due to increased noise-induced drift. (A) Decoding by the population vector of the WM activity pattern drifts across a 6-s delay. Left: Shown are 5 example traces each for control (blue) and disinhibition (orange). Right: Histogram of reports at the end of a 3-s delay. (B) The variance of the encoded angle fluctuations grows with time during the delay. Variance starts from a similar value for the 2 conditions, but the variance grows at a higher rate under disinhibition. (C) Visualization of reports after a 3-s delay for a memory-guided saccade task, with 96 trials at each of 8 stimulus angles. Gray circles mark the accuracy equivalent to within ±10° in the 1-dimensional model. (D) Error rates. Errors are counted if the reports shown in C lie outside of their corresponding gray circles. (E) Increase of the report variance, after a 3-s delay, as a function of the degree of disinhibition.

Figure 5.

Disinhibition is detrimental the network's ability to filter out nonlocal distractors. (A) Spatiotemporal plot of network activity in response to a distractor presented during the delay at a distance of 90° from the target. The 250-ms distractor is presented at 1.5 s into the delay. (B) Deviation of report, read out after a 3-s delay, as a function of the angular distance between the distractor and the target. The angle corresponding to maximum displacement defines the distractibility window. The distractibility window is widened by disinhibition. Error bars show standard deviation. (C) Increase of the distractibility window as a function of the degree of disinhibition.

Figure 6.

Ketamine induces errors in spatial WM specifically for near distractors, as predicted by the model. (A) Experimental task design. Subjects encoded spatial targets in WM, then after a delay, responded whether a probe matched a target location (match) or not (nonmatch). (B) Pattern of error rates for human subjects under placebo and ketamine. Ketamine degrades performance selectively, increasing false alarms to near distractor probes (**P < 0.01), but not false alarms to far distractor probes or misses to target probes (not significant). Here we computed error rates for each subject to allow comparison to model performance; comprehensive statistics in the main text pertain to original error counts across relevant factors of interest. (C) Illustration of how disinhibition in the WM model affects match/nonmatch decisions. Overlap between the probe representations (gray) and the target representation in WM is larger for disinhibition (orange) than for control (blue). Overlap increases most for the near distractor location. Inset: The probability of a match response is given by a sigmoidal function of the overlap. (D) Model performance under disinhibition replicates the observation of the human study that the error rate is differentially increased for false alarms to near distractor probes. As shown in C, we modeled probe locations as 60° and 120° from the target for near and far distractors, respectively.

Figure 7.

Compensations can restore E/I balance and ameliorate behavioral deficits. Disinhibition is combined with either suppression of presynaptic glutamate release at recurrent synapses (purple) or enhanced GABAR conductance on pyramidal cells (green). (A) Bell-shaped persistent activity profile (WM bump) for control, disinhibition, and the 2 compensation conditions. (B) The compensations restore the bump width from its broadened value under disinhibition to near its control value. (C) The disinhibition of the spontaneous firing rate is ameliorated by the compensations, particularly the glutamatergic manipulation. (D) The report variance, after a 3-s delay, is reduced to near the control level by the compensations. (E) The distractibility window is reduced to near the control level by the compensations.