Schizophrenia is associated with a pattern of spatial working memory deficits consistent with cortical disinhibition

Abstract

Schizophrenia is associated with severe cognitive deficits, including impaired working memory (WM). A neural mechanism that may contribute to WM impairment is the disruption in excitation-inhibition (E/I) balance in cortical microcircuits. It remains unknown, however, how these alterations map onto quantifiable behavioral deficits in patients. Based on predictions from a validated microcircuit model of spatial WM, we hypothesized two key behavioral consequences: i) increased variability of WM traces over time, reducing performance precision; and ii) decreased ability to filter out distractors that overlap with WM representations. To test model predictions, we studied N=27 schizophrenia patients and N=28 matched healthy comparison subjects (HCS) who performed a spatial WM task designed to test the computational model. Specifically, we manipulated delay duration and distractor distance presented during the delay. Subjects used a high-sensitivity joystick to indicate the remembered location, yielding a continuous response measure. Results largely followed model predictions, whereby patients exhibited increased variance and less WM precision as the delay period increased relative to HCS. Schizophrenia patients also exhibited increased WM distractibility, with reports biased toward distractors at specific spatial locations, as predicted by the model. Finally, the magnitude of the WM drift and distractibility were significantly correlated, indicating a possibly shared underlying mechanism. Effects are consistent with elevated E/I ratio in schizophrenia, establishing a framework for translating neural circuit computational model of cognition to human experiments, explicitly testing mechanistic behavioral hypotheses of cellular-level neural deficits in patients.

Keywords: Cognitive deficits; Computational modeling; Disinhibition; Excitation/inhibition balance; Schizophrenia; Working memory.

Fig. 1

Working memory paradigm. Subjects were asked to remember the position of circles (d = 125px) that were presented at 20 pseudo-randomly chosen angles along a hidden radial grid (r = 415px). This was done to mimic the ‘ring’ structure of the biophysically-based computational model motivating the design (Compte et al., 2000). After a delay subjects used a high-sensitivity joystick to indicate the remembered location, providing a parametric index of accuracy (as opposed to a forced-choice yes/no answer). The delay period varied parametrically such that subjects were asked to hold the location in memory for 0 s (i.e. immediate recall), 5 s, 10 s, 15 s, or 20s (60–20 trials). Subjects also completed a series of trials that contained a distractor, such that an additional circle appeared that subjects did not have to remember. During the distractor task the delay period was always 10s and the distractor appeared in the middle of the delay (after 4.3 s). There were two types of distractors, appearing at either 20° (proximal distractors) or 50° (distal distractors) from the original cue position (40 trials each). Lastly, subjects completed a control motor task (not shown) where cue circle and probe circle appeared simultaneously, requiring subjects to place the probe on top of the cue circle which necessitated a motor response but no WM maintenance or recall (20 trials).

Fig. 2

Computational modeling results and experimental predictions. A. In the cortical circuit model of spatial WM, a brief stimulus excites a subset of neurons, which encode the stimulus across the delay through a pattern of persistent activity that is shaped by recurrent excitation and lateral inhibition. Disinhibition is implemented via reduction of NMDAR conductance from pyramidal cells onto inhibitory interneurons (G_EI) (Murray et al., 2014). Disinhibition results in a broadened WM representation. B. The variability of WM report increases with delay duration. Disinhibition increases the rate at which variability increases, constituting a deficit in WM maintenance. C. Distractors are modeled as an intervening stimulus inputs during the delay. Under the control condition, there is no overlap between distractor and WM representations, and the report is unperturbed by the distractor. Under disinhibition, there is overlap and the report is shifted toward the distractor location. D. The mean WM report is shifted in the direction of the distractor, with dependence on the angular separation between distractor and cue. Disinhibition shows a larger distractibility window, i.e. the separation with maximal impact on WM report. E. The distractibility window and WM variability (chosen here at 3-s delay, no-distractor condition) are correlated as both smoothly increase with the strength of disinhibition (reduction of G_EI). Panels A-D adapted from (Murray et al., 2014) with permission.

Fig. 3. Effects of Delay Duration on SWM Performance

Effects of delay duration on working memory drift. A. Each panel indicates positions of responses on the screen at different delays. Results are rotated such that every cue is presented at 0° angle to facilitate visual inspection. Gray dots indicate the pseudo-random positions of targets on the screen before rotation for analysis purposes. 95% confidence ellipses are shown around the mean response pattern for each group. Responses spread with increasing delay but remain centered on the cue position. B. Average standard deviation of response errors. Average variability is increased for the SCZ group compared to HCS and this difference increased with Delay Duration, indicating lower WM precision. C. As expected, average angular displacement of the responses is unaffected by either Delay Duration or Diagnosis, indicating no directional bias in the response pattern, as predicted by the model. D. Effect sizes for between-group differences shown in B are in the medium to large range and remain relatively stable as delay increases, with the only notable increase at the longest delay. E. Average drift rate across the five delays shown in B is increased for the SCZ group compared to HCS. Error bars show ± 1 standard error of the mean.

Fig. 4. Effects of Distractor Distance on Spatial Working Memory Performance

Effects of distractor position on working memory performance. A. Each panel indicates positions of responses on the screen rotated to angle 0 with 95% confidence ellipses and gray dots marking target positions before rotation, as in Fig. 3. Responses for the SCZ group show spreading in the presence of distractors, displaced away from cues toward distractors (crosses), in the distal distraction condition. B. Average standard deviation of response errors. Variability is increased in the SCZ group compared to HCS and in both groups in conditions with distractors. C. Distractors cause angular displacement of responses. While proximal distractors bias both groups to move away from the distractor, distal distractors only affect the SCZ group by biasing their responses toward the distractor location. Error bars show ± 1 standard error of the mean. D. Density plots for HCS (left) and SCZ (right) illustrating the shifts in responses. Critically, these density plots are not consistent with bi-modal patterns of responses, but rather indicate subtle general right distribution shift for SCZ following distal distractors.

Fig. 5

Positive relationship between working memory drift and distractibility. Linear regression was used to calculate the slope of working memory drift over time for each subject The resulting regression slopes (β values) for each subject were correlated with angular displacement in the distal distractor condition. The black line shows the positive correlation between the two—the more WM drift with increasing delay, the greater the distractor bias—suggesting possible shared mechanisms (Fig. 2E) (Murray et al., 2014). Here we collapsed the analysis across both SCZ and HCS to maximize power given that spatial WM is a continuous measure and a construct highly consistent with a ‘dimensional’ perturbation (Cuthbert and Insel, 2013).