NMDA receptor function in large-scale anticorrelated neural systems with implications for cognition and schizophrenia

Abstract

Glutamatergic neurotransmission mediated by N-methyl-d-aspartate (NMDA) receptors is vital for the cortical computations underlying cognition and might be disrupted in severe neuropsychiatric illnesses such as schizophrenia. Studies on this topic have been limited to processes in local circuits; however, cognition involves large-scale brain systems with multiple interacting regions. A prominent feature of the human brain's global architecture is the anticorrelation of default-mode vs. task-positive systems. Here, we show that administration of an NMDA glutamate receptor antagonist, ketamine, disrupted the reciprocal relationship between these systems in terms of task-dependent activation and connectivity during performance of delayed working memory. Furthermore, the degree of this disruption predicted task performance and transiently evoked symptoms characteristic of schizophrenia. We offer a parsimonious hypothesis for this disruption via biophysically realistic computational modeling, namely cortical disinhibition. Together, the present findings establish links between glutamate's role in the organization of large-scale anticorrelated neural systems, cognition, and symptoms associated with schizophrenia in humans.

Fig. 1.

(A) Subjects encoded four or two (not shown; see Methods) circle locations and, after a delay, indicated whether the circle was presented at that location or not (probe). Subjects also completed a control task where four gray circles appeared but were explicitly asked not to encode the circles. During the probe phase, another gray circle was shown, requiring a motor response but no recall. (B) Second-level conjunction fMRI analysis strategy: (i) we computed a main effect of task (i.e., WM vs. control condition) type I error corrected at the whole-brain level (Fig. 2); and (ii) we computed a task × infusion interaction, revealing regions differentially modulated by ketamine across task conditions. Regions identified this way are not guaranteed to be involved in WM. That is, regions showing a task × infusion interaction may not show engagement during WM (i.e., main effect of task). Thus, we computed a conjunction (logical AND) between these effects. The surviving regions were ensured to show both a task main effect and modulation of this effect by ketamine (Fig. 3A and Fig. S2). (C) Percentage drop in accuracy (% correct) is shown for the control (white bar) and WM (black bar) tasks following ketamine vs. placebo infusion (difference plotted). ****P < 0.0001 (see SI Text, Behavioral Results for complete behavioral analysis). Error bars reflect ±1 SEM.

Fig. 2.

WM effects for encoding (A), delay (B), and probe (C) phases are shown. Maps illustrate task-based WM activations (orange-yellow) and deactivations (blue) (Left and Right, respectively). All displayed foci met a whole-brain correction (SI Text, fMRI Analyses). Time courses are shown for exemplar regions identified using an assumed hemodynamic response function (SI Text, fMRI Analyses), exhibiting significant WM (black squares) vs. control task (dashed lines) effects. Approximate trial epochs (encoding, delay, probe) are marked with gray vertical bars. Canonical WM responses are evident across all epochs. Region coordinates are marked in boxes. For complete list of task-modulated foci, see Tables S1–S3.

Fig. 3.

(A) Regions exhibiting WM effects and modulation of this effect by ketamine for task-based activation (Upper) and task-based deactivation (Lower). WM time courses are shown for dorso-lateral prefrontal cortex (Upper) and precuneus (Lower) for ketamine (red) and placebo (blue) (coordinates are shown in boxes). Complete list of foci exhibiting significant effects across encoding and delay phases is presented in Table S4 and Fig. S2. The probe phase analysis did not reveal significant modulation by ketamine. Note: less negative value for DMN under ketamine reflects less deactivation relative to baseline compared with the placebo condition. (B) Computational model scheme, comprised of task-activated (Upper) and task-deactivated (Lower) modules followed by modeling results. We modeled the effects of ketamine as a reduction of NMDA conductance onto inhibitory interneurons (g_E-I). We examined whether “disinhibition” via reduced NMDA conductance onto GABA cells (E-I) would result in effects similar to BOLD findings under ketamine. Here, we present predicted BOLD signal derived from the simulated local field potential (LFP) on the time scale comparable to a single WM trial in the experiment to appropriately juxtapose model simulations to BOLD empirical observations. For complete modeling implementation, BOLD simulation details, and comparison with firing-rate results, see SI Text, Computational Modeling and Figs. S4–S6.

Fig. 4.

tb-fcMRI during the delay phase between independently selected FP and DMN regions following ketamine (red) and placebo (blue) infusion, shown for the control (CT) and WM task conditions (all seed coordinates are listed in Table S5; for tb-fcMRI details, see SI Text, tb-fcMRI). Error bars reflect ±1 SEM.

Fig. 5.

(A) Correct vs. incorrect WM time courses are shown across placebo (blue) and ketamine (red) for the precuneus region that exhibited effects of ketamine on WM (defined via the conjunction approach, box shows region coordinates). (B) Relationship between the magnitude of DMN deactivation during WM following ketamine administration and severity of negative symptoms associated with schizophrenia. Subjects showing least DMN suppression during WM were most symptomatic. This effect was specific to WM trials; during control trials under ketamine, this relationship was not evident (r = 0.11).