Country-level gender inequality is associated with structural differences in the brains of women and men.

Gender inequality across the world has been associated with a higher risk to mental health problems and lower academic achievement in women compared to men. We also know that the brain is shaped by nurturing and adverse socio-environmental experiences. Therefore, unequal exposure to harsher conditions for women compared to men in gender-unequal countries might be reflected in differences in their brain structure, and this could be the neural mechanism partly explaining women's worse outcomes in gender-unequal countries. We examined this through a random-effects meta-analysis on cortical thickness and surface area differences between adult healthy men and women, including a meta-regression in which country-level gender inequality acted as an explanatory variable for the observed differences. A total of 139 samples from 29 different countries, totaling 7,876 MRI scans, were included. Thickness of the right hemisphere, and particularly the right caudal anterior cingulate, right medial orbitofrontal, and left lateral occipital cortex, presented no differences or even thicker regional cortices in women compared to men in gender-equal countries, reversing to thinner cortices in countries with greater gender inequality. These results point to the potentially hazardous effect of gender inequality on women's brains and provide initial evidence for neuroscience-informed policies for gender equality.

Cholinergic Neuromodulation of Prefrontal Attractor Dynamics Controls Performance in Spatial Working Memory.

The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the nucleus basalis (NB) of Meynert have been recently examined in two male monkeys (Qi et al., 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in the PFC.

Correction: Pinging the brain with visual impulses reveals electrically active, not activity-silent, working memories.

[This corrects the article DOI: 10.1371/journal.pbio.3001436.].

Pinging the brain with visual impulses reveals electrically active, not activity-silent, working memories.

Persistently active neurons during mnemonic periods have been regarded as the mechanism underlying working memory maintenance. Alternatively, neuronal networks could instead store memories in fast synaptic changes, thus avoiding the biological cost of maintaining an active code through persistent neuronal firing. Such "activity-silent" codes have been proposed for specific conditions in which memories are maintained in a nonprioritized state, as for unattended but still relevant short-term memories. A hallmark of this "activity-silent" code is that these memories can be reactivated from silent, synaptic traces. Evidence for "activity-silent" working memory storage has come from human electroencephalography (EEG), in particular from the emergence of decodability (EEG reactivations) induced by visual impulses (termed pinging) during otherwise "silent" periods. Here, we reanalyze EEG data from such pinging studies. We find that the originally reported absence of memory decoding reflects weak statistical power, as decoding is possible based on more powered analyses or reanalysis using alpha power instead of raw voltage. This reveals that visual pinging EEG "reactivations" occur in the presence of an electrically active, not silent, code for unattended memories in these data. This crucial change in the evidence provided by this dataset prompts a reinterpretation of the mechanisms of EEG reactivations. We provide 2 possible explanations backed by computational models, and we discuss the relationship with TMS-induced EEG reactivations.

StimuliApp: Psychophysical tests on mobile devices.

Psychophysical tests are commonly carried out using software applications running on desktop or laptop computers, but running the software on mobile handheld devices such as smartphones or tablets could have advantages in some situations. Here, we present StimuliApp, an open-source application in which the user can create psychophysical tests on the iPad and the iPhone by means of a system of menus. A wide number of templates for creating stimuli are available including patches, gradients, gratings, checkerboards, random-dots, texts, tones or auditory noise. Images, videos and audios stored in files could also be presented. The application was developed natively for iPadOS and iOS using the low-level interface Metal for accessing the graphics processing unit, which results in high timing performance.

Effect-Based Identification of Hazardous Antibiotic Transformation Products after Water Chlorination.

Antibiotic transformation products (TPs) generated during water treatment can be considered as an environmental concern, since they can retain part of the bioactivity of the parent compound. Effect-directed analysis (EDA) was applied for the identification of bioactive intermediates of azithromycin (AZI) and ciprofloxacin (CFC) after water chlorination. Fractionation of samples allowed the identification of bioactive intermediates by measuring the antibiotic activity and acute toxicity, combined with an automated suspect screening approach for chemical analysis. While the removal of AZI was in line with the decrease of bioactivity in chlorinated samples, an increase of bioactivity after complete removal of CFC was observed (at >0.5 mgCl2/L). Principal component analysis (PCA) revealed that some of the CFC intermediates could contribute to the overall toxicity of the chlorinated samples. Fractionation of bioactive samples identified that the chlorinated TP296 (generated from the destruction of the CFC piperazine ring) maintained 41%, 44%, and 30% of the antibiotic activity of the parent compound in chlorinated samples at 2.0, 3.0, and 4.0 mgCl2/L, respectively. These results indicate the spectrum of antibacterial activity can be altered by controlling the chemical substituents and configuration of the CFC structure with chlorine. On the other hand, the potential presence of volatile DBPs and fractionation losses do not allow for tentative confirmation of the main intermediates contributing to the acute toxic effects measured in chlorinated samples. Our results encourage further development of new and advanced methodologies to study the bioactivity of isolated unknown TPs to understand their hazardous effects in treated effluents.

Comprehensive study of sulfamethoxazole effects in marine mussels: Bioconcentration, enzymatic activities and metabolomics.

Antibiotics accumulation in aquatic organisms may be of great concern from an ecological point of view but also from a human perspective, especially when they are accumulated in edible animals like marine mussels. In this work, mussels (Mytilus galloprovincialis) were exposed to sulfamethoxazole antibiotic (SMX) at 10 µg/L during 96 h, followed by 24 h of depuration. The experiment was carried out at summer and winter conditions. SMX showed a bioconcentration factor in mussel of 1.5 L/kg (dry weight) and 69% of the compound was eliminated from the organism in 24 h. The metabolomics approach revealed alterations in amino acids levels (aspartate, phenylalanine, valine and tryptophan) pinpointing disturbances in osmotic regulation and energy metabolism. Besides, the levels of some nucleotides (guanosine and inosine) and a carboxylic acid were also affected. However, SMX exposed mussels did not show any significant alteration in the enzymatic activities related to the xenobiotic metabolism and oxidative stress. Moreover, some of the changes observed in mussel's metabolites suggested alterations in mussel's organoleptic characteristics that can affect its quality as seafood commodity. Overall, our results showed that SMX exposure to marine mussels may have ecological implications by provoking sub-lethal effects to exposed organisms. Nevertheless, no risk for consumers derived from mussel ingestion is expected due to the low bioconcentration capacity of SMX and fast depuration in this seafood type.

A Computational Model of Major Depression: the Role of Glutamate Dysfunction on Cingulo-Frontal Network Dynamics.

Major depression disease (MDD) is associated with the dysfunction of multinode brain networks. However, converging evidence implicates the reciprocal interaction between midline limbic regions (typified by the ventral anterior cingulate cortex, vACC) and the dorso-lateral prefrontal cortex (dlPFC), reflecting interactions between emotions and cognition. Furthermore, growing evidence suggests a role for abnormal glutamate metabolism in the vACC, while serotonergic treatments (selective serotonin reuptake inhibitor, SSRI) effective for many patients implicate the serotonin system. Currently, no mechanistic framework describes how network dynamics, glutamate, and serotonin interact to explain MDD symptoms and treatments. Here, we built a biophysical computational model of 2 areas (vACC and dlPFC) that can switch between emotional and cognitive processing. MDD networks were simulated by slowing glutamate decay in vACC and demonstrated sustained vACC activation. This hyperactivity was not suppressed by concurrent dlPFC activation and interfered with expected dlPFC responses to cognitive signals, mimicking cognitive dysfunction seen in MDD. Simulation of clinical treatments (SSRI or deep brain stimulation) counteracted this aberrant vACC activity. Theta and beta/gamma oscillations correlated with network function, representing markers of switch-like operation in the network. The model shows how glutamate dysregulation can cause aberrant brain dynamics, respond to treatments, and be reflected in EEG rhythms as biomarkers of MDD.

Transitions between Multiband Oscillatory Patterns Characterize Memory-Guided Perceptual Decisions in Prefrontal Circuits.

Neuronal activity in the lateral prefrontal cortex (LPFC) reflects the structure and cognitive demands of memory-guided sensory discrimination tasks. However, we still do not know how neuronal activity articulates in network states involved in perceiving, remembering, and comparing sensory information during such tasks. Oscillations in local field potentials (LFPs) provide fingerprints of such network dynamics. Here, we examined LFPs recorded from LPFC of macaques while they compared the directions or the speeds of two moving random-dot patterns, S1 and S2, separated by a delay. LFP activity in the theta, beta, and gamma bands tracked consecutive components of the task. In response to motion stimuli, LFP theta and gamma power increased, and beta power decreased, but showed only weak motion selectivity. In the delay, LFP beta power modulation anticipated the onset of S2 and encoded the task-relevant S1 feature, suggesting network dynamics associated with memory maintenance. After S2 onset the difference between the current stimulus S2 and the remembered S1 was strongly reflected in broadband LFP activity, with an early sensory-related component proportional to stimulus difference and a later choice-related component reflecting the behavioral decision buildup. Our results demonstrate that individual LFP bands reflect both sensory and cognitive processes engaged independently during different stages of the task. This activation pattern suggests that during elementary cognitive tasks, the prefrontal network transitions dynamically between states and that these transitions are characterized by the conjunction of LFP rhythms rather than by single LFP bands. SIGNIFICANCE STATEMENT: Neurons in the brain communicate through electrical impulses and coordinate this activity in ensembles that pulsate rhythmically, very much like musical instruments in an orchestra. These rhythms change with "brain state," from sleep to waking, but also signal with different oscillation frequencies rapid changes between sensory and cognitive processing. Here, we studied rhythmic electrical activity in the monkey prefrontal cortex, an area implicated in working memory, decision making, and executive control. Monkeys had to identify and remember a visual motion pattern and compare it to a second pattern. We found orderly transitions between rhythmic activity where the same frequency channels were active in all ongoing prefrontal computations. This supports prefrontal circuit dynamics that transitions rapidly between complex rhythmic patterns during structured cognitive tasks.

Neural circuit basis of visuo-spatial working memory precision: a computational and behavioral study.

The amount of information that can be retained in working memory (WM) is limited. Limitations of WM capacity have been the subject of intense research, especially in trying to specify algorithmic models for WM. Comparatively, neural circuit perspectives have barely been used to test WM limitations in behavioral experiments. Here we used a neuronal microcircuit model for visuo-spatial WM (vsWM) to investigate memory of several items. The model assumes that there is a topographic organization of the circuit responsible for spatial memory retention. This assumption leads to specific predictions, which we tested in behavioral experiments. According to the model, nearby locations should be recalled with a bias, as if the two memory traces showed attraction or repulsion during the delay period depending on distance. Another prediction is that the previously reported loss of memory precision for an increasing number of memory items (memory load) should vanish when the distances between items are controlled for. Both predictions were confirmed experimentally. Taken together, our findings provide support for a topographic neural circuit organization of vsWM, they suggest that interference between similar memories underlies some WM limitations, and they put forward a circuit-based explanation that reconciles previous conflicting results on the dependence of WM precision with load.

Serotonin regulates performance nonmonotonically in a spatial working memory network.

The prefrontal cortex (PFC) contains a dense network of serotonergic [serotonin, 5-hydroxytryptamine (5-HT)] axons, and endogenous 5-HT markedly modulates PFC neuronal function via several postsynaptic receptors. The therapeutic action of atypical antipsychotic drugs, acting mainly via 5-HT receptors, also suggests a role for serotonergic neurotransmission in cognitive functions. However, psychopharmacological studies have failed to find a consistent relationship between serotonergic transmission and cognitive functions of the PFC, including spatial working memory (SWM). Here, we built a computational network model to investigate 5-HT modulation of SWM in the PFC. We found that 5-HT modulates network's SWM performance nonmonotonically via 5-HT1A and 5-HT2A receptors, following an inverted U-shape. This relationship may contribute to blur the effects of serotonergic agents in previous SWM group-based behavioral studies. Our simulations also showed that errors occurring at low and high 5-HT concentrations are due to different network dynamics instabilities, suggesting that these 2 conditions can be distinguished experimentally based on their distinct dependency on experimental variables. We inferred specific predictions regarding the expected behavioral effects of serotonergic agents in 2 classic working-memory tasks. Our results underscore the relevance of identifying different error types in SWM tasks in order to reveal the association between neuromodulatory systems and SWM.

Trade-off between capacity and precision in visuospatial working memory.

Limitations in the performance of working memory (WM) tasks have been characterized in terms of the number of items retained (capacity) and in terms of the precision with which the information is retained. The neural mechanisms behind these limitations are still unclear. Here we used a biological constrained computational model to study the capacity and precision of visuospatial WM. The model consists of two connected networks of spiking neurons. One network is responsible for storage of information. The other provides a nonselective excitatory input to the storage network. Simulations showed that this excitation boost could temporarily increase storage capacity but also predicted that this would be associated with a decrease in precision of the memory. This prediction was subsequently tested in a behavioral (38 participants) and fMRI (22 participants) experiment. The behavioral results confirmed the trade-off effect, and the fMRI results suggest that a frontal region might be engaged in the trial-by-trial control of WM performance. The average effects were small, but individuals differed in the amount of trade-off, and these differences correlated with the frontal activation. These results support a two-module model of WM where performance is determined both by storage capacity and by top-down influence, which can vary on a trial-by-trial basis, affecting both the capacity and precision of WM.

Cholinergic neuromodulation of prefrontal attractor dynamics controls performance in spatial working memory.

The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the Nucleus Basalis of Meynert (NB) have been recently examined (Qi et al. 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in PFC.

Alpha phase-coding supports feature binding during working memory maintenance.

The ability to successfully retain and manipulate information in working memory (WM) requires that objects' individual features are bound into cohesive representations; yet, the mechanisms supporting feature binding remain unclear. Binding (or swap) errors, where memorized features are erroneously associated with the wrong object, can provide a window into the intrinsic limits in capacity of WM that represent a key bottleneck in our cognitive ability. We tested the hypothesis that binding in WM is accomplished via neural phase synchrony and that swap errors result from perturbations in this synchrony. Using magnetoencephalography data collected from human subjects in a task designed to induce swap errors, we showed that swaps are characterized by reduced phase-locked oscillatory activity during memory retention, as predicted by an attractor model of spiking neural networks. Further, we found that this reduction arises from increased phase-coding variability in the alpha-band over a distributed network of sensorimotor areas. Our findings demonstrate that feature binding in WM is accomplished through phase-coding dynamics that emerge from the competition between different memories.

Neurological, psychiatric, and sleep investigations after treatment of anti-leucine-rich glioma-inactivated protein 1 (LGI1) encephalitis in Spain: a prospective cohort study.

BACKGROUND: Anti-leucine-rich glioma-inactivated protein 1 (LGI1) encephalitis is an autoimmune disorder that can be treated with immunotherapy, but the symptoms that remain after treatment have not been well described. We aimed to characterise the clinical features of patients with anti-LGI1 encephalitis for 1 year starting within the first year after initial immunotherapy. METHODS: For this prospective cohort study, we recruited patients with anti-LGI1 encephalitis as soon as possible after they had received conventional immunotherapy for initial symptoms; patients were recruited from 21 hospitals in Spain. Patients were excluded if they had an interval of more than 1 year since initial immunotherapy, had pre-existing neurodegenerative or psychiatric disorders, or were unable to travel to Hospital Clínic de Barcelona (Barcelona, Spain). Patients visited Hospital Clínic de Barcelona on three occasions-the first at study entry (visit 1), the second 6 months later (visit 2), and the third 12 months after the initial visit (visit 3). They underwent neuropsychiatric and videopolysomnography assessments at each visit. Healthy participants who were matched for age and sex and recruited from Hospital Clínic de Barcelona underwent the same investigations at study entry and at 12 months. Cross-sectional comparisons of clinical features between groups were done with conditional logistic regression, and binary logistic regression was used to assess associations between cognitive outcomes at 12 months and clinical features before initial immunotherapy and at study entry. FINDINGS: Between May 1, 2019, and Sept 30, 2022, 42 participants agreed to be included in this study. 24 (57%) participants had anti-LGI1 encephalitis (mean age 63 years [SD 12]; 13 [54%] were female and 11 [46%] were male) and 18 (43%) were healthy individuals (mean age 62 years [10]; 11 [61%] were female and seven [39%] were male). At visit 1 (median 88 days [IQR 67-155] from initiation of immunotherapy), all 24 patients had one or more symptoms; 20 (83%) patients had cognitive deficits, 20 (83%) had psychiatric symptoms, 14 (58%) had insomnia, 12 (50%) had rapid eye movement (REM)-sleep behaviour disorder, nine (38%) had faciobrachial dystonic seizures, and seven (29%) had focal onset seizures. Faciobrachial dystonic seizures were unnoticed in four (17%) of 24 patients and focal onset seizures were unnoticed in five (21%) patients. At visit 1, videopolysomnography showed that 19 (79%) patients, but no healthy participants, had disrupted sleep structure (p=0·013); 15 (63%) patients and four (22%) healthy participants had excessive fragmentary myoclonus (p=0·039), and nine (38%) patients, but no healthy participants, had myokymic discharges (p=0·0051). These clinical and videopolysomnographic features led to additional immunotherapy in 15 (63%) of 24 patients, which resulted in improvement of these features in all 15 individuals. However, at visit 3, 13 (65%) of 20 patients continued to have cognitive deficits. Persistent cognitive deficits at visit 3 were associated with no use of rituximab before visit 1 (odds ratio [OR] 4·0, 95% CI 1·5-10·7; p=0·0015), REM sleep without atonia at visit 1 (2·2, 1·2-4·2; p=0·043), and presence of LGI1 antibodies in serum at visit 1 (11·0, 1·1-106·4; p=0·038). INTERPRETATION: Unsuspected but ongoing clinical and videopolysomnography alterations are common in patients with anti-LGI1 encephalitis during the first year or more after initial immunotherapy. Recognising these alterations is important as they are treatable, can be used as outcome measures in clinical trials, and might influence cognitive outcome. FUNDING: Fundació La Caixa.

Effects of sulfamethoxazole exposure on mussels (Mytilus galloprovincialis) metabolome using retrospective non-target high-resolution mass spectrometry and chemometric tools.

In this work, the Regions of Interest-Multivariate Curve Resolution (ROIMCR) method is proposed for the analysis of non-target metabolomics data. Samples from marine mussels (Mytilus galloprovincialis) exposed to a sublethal concentration (10 µg/L) of sulfamethoxazole (SMX) during 4 days in different seasonal conditions (summer and winter) were analyzed by High-Performance Liquid Chromatography - High-Resolution Mass Spectrometry (HPLC-HRMS) to study the effect of their exposure to SMX and the different seasonal conditions. The Regions of Interest (ROI) procedure has been applied for data filtering, compression, preprocessing and storage steps. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) is then applied to the previously MS ROI preprocessed data sets to resolve the elution profiles and spectral fingerprints of the chemical constituents of the analyzed samples. The peak areas of the elution profiles of the chemical constituents resolved by the combined ROIMCR procedure were analyzed by Principal Component Analysis (PCA) and samples were clustered according to their experimental seasonal and SMX exposure. The effects of the two investigated factors and of their interaction on the concentrations of the metabolites were statistically assessed by ANOVA simultaneous component analysis (ASCA). Both types of analyses, PCA clustering and ASCA, confirmed that the seasonal conditions (summer versus winter) produced larger effects than those produced by the exposure to SMX and by the interaction of these two factors. The concentration changes of 16 identified endogenous metabolites were validated individually using a Wilcoxon statistical test, which confirmed the presence of significant disturbances in the levels of some of these metabolites (free fatty acids, amino acids and nucleic acids), and indicated the possible alteration of six different biological pathways, affected by the investigated seasonal and SMX exposure factors.

Mechanism for top-down control of working memory capacity.

Working memory capacity, the maximum number of items that we can transiently store in working memory, is a good predictor of our general cognitive abilities. Neural activity in both dorsolateral prefrontal cortex and posterior parietal cortex has been associated with memory retention during visuospatial working memory tasks. The parietal cortex is thought to store the memories. However, the role of the dorsolateral prefrontal cortex, a top-down control area, during pure information retention is debated, and the mechanisms regulating capacity are unknown. Here, we propose that a major role of the dorsolateral prefrontal cortex in working memory is to boost parietal memory capacity. Furthermore, we formulate the boosting mechanism computationally in a biophysical cortical microcircuit model and derive a simple, explicit mathematical formula relating memory capacity to prefrontal and parietal model parameters. For physiologically realistic parameter values, lateral inhibition in the parietal cortex limits mnemonic capacity to a maximum of 2-7 items. However, at high loads inhibition can be counteracted by excitatory prefrontal input, thus boosting parietal capacity. Predictions from the model were confirmed in an fMRI study. Our results show that although memories are stored in the parietal cortex, interindividual differences in memory capacity are partly determined by the strength of prefrontal top-down control. The model provides a mechanistic framework for understanding top-down control of working memory and specifies two different contributions of prefrontal and parietal cortex to working memory capacity.

Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia.

While wastewater-based epidemiology has proven a useful tool for epidemiological surveillance during the COVID-19 pandemic, few quantitative models comparing virus concentrations in wastewater samples and cumulative incidence have been established. In this work, a simple mathematical model relating virus concentration and cumulative incidence for full contagion waves was developed. The model was then used for short-term forecasting and compared to a local linear model. Both scenarios were tested using a dataset composed of samples from 32 wastewater treatment plants and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence data covering the corresponding geographical areas during a 7-month period, including two contagion waves. A population-averaged dataset was also developed to model and predict the incidence over the full geography. Overall, the mathematical model based on wastewater data showed a good correlation with cumulative cases and allowed us to anticipate SARS-CoV-2 incidence in one week, which is of special relevance in situations where the epidemiological monitoring system cannot be fully implemented.

Clinical characterisation of patients in the post-acute stage of anti-NMDA receptor encephalitis: a prospective cohort study and comparison with patients with schizophrenia spectrum disorders.

BACKGROUND: Anti-NMDA receptor (NMDAR) encephalitis is associated with a post-acute stage that is not well known. We aimed to describe the clinical features of this stage, similarities with schizophrenia spectrum disorders, and the factors that predict cognitive-psychiatric outcomes and could serve as prognostic biomarkers. METHODS: In this prospective cohort study, participants (aged 12-60 years) with anti-NMDAR encephalitis during the post-acute stage visited Hospital Clínic de Barcelona (Barcelona, Spain) on three occasions (at study entry [V1], at 6 months [V2], and at 12 months [V3]) and underwent comprehensive neuropsychiatric evaluations. Similar evaluations were done in a group of age-matched participants with schizophrenia spectrum disorders and a group of age-matched and sex-matched healthy participants also recruited from Hospital Clínic de Barcelona. We analysed differences between and within groups in the longitudinal follow-up using multilevel linear mixed-effect models, adjusting for group, age, sex, and socioeconomic status to control for possible confounding. FINDINGS: Between Jan 1, 2017, and Sept 30, 2020, 82 participants were recruited, 28 (34%) with anti-NMDAR encephalitis, 27 (33%) with schizophrenia spectrum disorders, and 27 (33%) healthy participants. Although, by V1 (median 4 months [IQR 3-7] from disease onset), many acute-stage symptoms in participants with anti-NMDAR encephalitis had resolved (acute stage median modified Rankin Scale [mRS] score 5 [IQR 4-5] vs V1 mRS score 2 [1-2]; p<0·0001), 25 (89%) participants showed deficits in at least one cognitive domain. In this group, 15 (68%) of 22 cognitive domain variables were impaired at V1, whereas only eight (36%) were altered at V3 (p=0·016). In participants with schizophrenia spectrum disorders, 11 (50%) of 22 variables (all shared with participants with anti-NMDAR encephalitis) were impaired at V1, without changes at V3. Two acute-stage features of anti-NMDAR encephalitis (ie, decreased consciousness and no improvement within the first 4 weeks of treatment) predicted cognitive domain outcomes, and a visuospatial task (ie, serial biases) at V1 showed potential in predicting learning and memory outcomes. At V1, all psychiatric symptom clusters were similarly altered in participants with anti-NMDAR encephalitis and in those with schizophrenia spectrum disorders, but only those in individuals with anti-NMDAR encephalitis subsequently improved (p=0·031). The greatest cognitive-psychiatric improvement in participants with anti-NMDAR encephalitis occurred between V1 and V2. During this interval, four (14%) participants with anti-NMDAR encephalitis would have met the diagnostic criteria of schizophrenia if CSF antibody findings had not been investigated. INTERPRETATION: The cognitive-psychiatric symptoms of anti-NMDAR encephalitis in the post-acute stage resembled those of stabilised schizophrenia, but only those in participants with anti-NMDAR encephalitis progressively improved, predominantly during V1-V2. These findings are important for clinical trials on anti-NMDAR encephalitis and suggest that prompt cognitive-psychosocial rehabilitation might be a valuable intervention. FUNDING: Instituto Salud Carlos III, NEURON Network of European Funding for Neuroscience Research, National Alliance for Research in Schizophrenia and Affective Disorders, and la Caixa Health-Research Foundation.

Reconciling coherent oscillation with modulation of irregular spiking activity in selective attention: gamma-range synchronization between sensory and executive cortical areas.

In this computational work, we investigated gamma-band synchronization across cortical circuits associated with selective attention. The model explicitly instantiates a reciprocally connected loop of spiking neurons between a sensory-type (area MT) and an executive-type (prefrontal/parietal) cortical circuit (the source area for top-down attentional signaling). Moreover, unlike models in which neurons behave as clock-like oscillators, in our model single-cell firing is highly irregular (close to Poisson), while local field potential exhibits a population rhythm. In this "sparsely synchronized oscillation" regime, the model reproduces and clarifies multiple observations from behaving animals. Top-down attentional inputs have a profound effect on network oscillatory dynamics while only modestly affecting single-neuron spiking statistics. In addition, attentional synchrony modulations are highly selective: interareal neuronal coherence occurs only when there is a close match between the preferred feature of neurons, the attended feature, and the presented stimulus, a prediction that is experimentally testable. When interareal coherence was abolished, attention-induced gain modulations of sensory neurons were slightly reduced. Therefore, our model reconciles the rate and synchronization effects, and suggests that interareal coherence contributes to large-scale neuronal computation in the brain through modest enhancement of rate modulations as well as a pronounced attention-specific enhancement of neural synchrony.