Schizophrenia (SZ) is a severe mental disorder with high heritability. DLG2 encodes the postsynaptic scaffolding protein DLG2 (PSD93, Postsynaptic Density Protein 93), and its variants were associated with an increased risk of SZ. However, the role of DLG2 locus variation in SZ remains elusive. This study aims to investigate the association between DLG2 gene polymorphisms and SZ susceptibility and the relationship between DLG2 and altered brain function and clinical symptoms in SZ patients.

Single nucleotide polymorphisms (SNPs) rs11607886 and rs7479949 were genotyped in 350 SZ patients and 407 healthy controls (HCs). 47 SZ patients and 79 HCs were genotyped into two groups: the risk A allele carrier group and the GG-pure group. Functional magnetic resonance imaging (fMRI) indices were further analyzed. Subsequently, data from different brain regions were correlated with clinical symptom assessment.

DLG2 rs11607886 was significantly associated with SZ. Significant main effects were found in the ALFF and ReHo, especially for the left precuneus gyrus (PCu). A significant interaction between genotype and diagnosis had a significant effect on FC, which was increased between the left PCu and the right middle temporal gyrus in carriers of the A allele with SZ (r = -0.336, Pun-corrected = 0.042) and negatively correlated with spatial breadth scores (r = 0.444, PFDR-corrected = 0.002).

The rs11607886 polymorphism in DLG2 may influence the pathogenesis of SZ and have potential effects on cognitive function. The present study emphasizes DLG2 as a candidate gene for SZ and suggests an important role for PCu in SZ.

Perception integrates external sensory signals with internal predictions that reflect prior knowledge about the world. Previous research suggests that this integration is governed by slow alternations between an external mode, driven by sensory signals, and an internal mode, shaped by prior knowledge. Using a double-blind, placebo-controlled, cross-over experiment in healthy human participants, we investigated the effects of the N-methyl-D-aspartate receptor (NMDAR) antagonist S-ketamine on the balance between external and internal modes. We found that S-ketamine causes a shift of perception towards the external mode. A case-control study revealed that individuals with paranoid schizophrenia, a disorder repeatedly associated with NMDAR hypofunction, spend more time in the external mode. This NMDAR-dependent increase in the external mode suggests that the symptoms of schizophrenia are caused by recurring dissociations of perception from prior knowledge about the world.

Depression is a chronic disorder that impacts millions worldwide. Traditional treatments may not always work. Inflammation seems to be an underlying cause for chronicity and treatment non-response.

We present a computational model that elucidates the interplay between inflammation, serotonin levels, and brain activity.

The model delineates how inflammation impacts extracellular serotonin, while cerebral activity reciprocally influences serotonin concentration. Understanding the reciprocal interplay between the immune system and brain dynamics is important, as unabated inflammation can lead to relapsing depression. The model predicts dynamics within the prefrontal cortex (PFC) and subcallosal cingulate cortex (SCC), mirroring patterns observed in depressive conditions. It also accommodates pharmaceutical interventions that encompass anti-inflammatory and antidepressant agents, concurrently evaluating their efficacy with regard to the severity of depressive symptoms Our model shows that for mild and moderate levels of depression anti-depressant agents or anti-inflammatory agents acting in isolation can bring serotonergic levels and brain activity to control levels. However, for severe depression only joint treatment of anti-depressant and anti-inflammatory agents can bring the serotonergic levels and activity to control levels.

This study is a first step to mechanistically understand the intricate link between the immune system and depression, the role of inflammation and potential treatments. It explores the impact of anti-depressant and anti-inflammatory drug treatments and assesses their relevance with regard to depression severity.

We present a neural mass model (NMM) of the brain thalamo-cortico-thalamic (TCT) network to understand the effectiveness of non-invasive treatment with transcranial Direct Current Stimulation (tDCS) in reversing the anomalous electroencephalogram (EEG) oscillations in Schizophrenia. Our TCT NMM consists of twelve neural populations representing the thalamus and cortex modules of the visual pathway connected in a closed loop; the synaptic pathways are modelled with a 3-state kinetic framework allowing the inclusion of the slow excitatory N-methyl-D-aspartate-receptors (NMDAR). Indeed, a popular hypothesis in Schizophrenia is the hypofunction of the Glutamatergic neurotransmitter receptors, NMDAR, associated with the inhibitory Gamma-amino-butyric-acid (GABA-)ergic populations in the cortex, leading to anomalous brain oscillations. Experimental studies simulate the EEG conditions in Schizophrenia by administering sub-anesthetic dosage of Ketamine, which blocks NMDAR channels at the Magnesium binding sites. We could simulate the Ketamine-induced NMDAR channel blocking by varying the Magnesium concentration in the 3-state synaptic models of appropriate pathways. Our results show Ketamine-induced increased excitatory behaviour in the model output; the changes in the γ and σ band oscillations conform to experimental studies. A model to factor in the neuroplasticity effects of applying tDCS (after (Riedinger and Hutt, 2022)) is interfaced with the TCT NMM. Informed by experimental literature, the simulated extrinsic current induced by tDCS is set to affect the plasticity in selected pathways. With appropriate parameterisation, we could simulate the reversal of the Ketamine-induced altered EEG oscillations. Overall, our in silico study emphasises the potential of NMM in predicting protocols for tDCS towards effective personalised treatment of Schizophrenia.

To discover biomarkers for schizophrenia (SCZ) at the metabolomics level, we registered this systematic review (CRD42024572133 (https://www.crd.york.ac.uk/PROSPERO/home)) including 56 qualified articles, and we identified the characteristics of metabolites, metabolite combinations, and metabolic pathways associated with SCZ.

Our findings showed that decreased arachidonic acid, arginine, and aspartate levels, and the increased levels of glucose 6-phosphate and glycylglycine were associated with the onset of SCZ. Metabolites such as carnitine and methionine sulfoxide not only helped to identify SCZ in Miao patients, but also were different between Miao patients and Han patients. The decrease in benzoic acid and betaine and the increase in creatine were the notable metabolic characteristics of first-episode schizophrenia (FESCZ). The metabolite combination formed by metabolites such as methylamine, dimethylamine and other metabolites had the best diagnostic effect. Arginine and proline metabolism and arginine biosynthesis had a clear advantage in identifying SCZ and acute SCZ. Butanoate metabolism played an important role in identifying SCZ, toxoplasma infection and SCZ comorbidity. Biosynthesis of unsaturated fatty acids was also significantly enriched in the diagnosis and treatment of SCZ.

This study summarizes the current progress in clinical metabolomic research related to SCZ, deepens understanding of the pathogenesis of SCZ, and lays a foundation for subsequent research on SCZ-related metabolites.

https://www.crd.york.ac.uk/PROSPERO/home, identifier CRD42024572133.

Intrinsic neural timescales, which reflect the duration of neural information storage within local brain regions and capacity for information integration, are typically measured using autocorrelation windows (ACWs). Extraction of intrinsic neural timescales from resting-state brain activity has been extensively applied in psychiatric disease research. Given the potential of intrinsic neural timescales as a neural marker for psychiatric disorders, investigating their reliability is crucial. This study, using an open-source database, aimed to evaluate the test-retest reliability of ACW-0 and ACW-50 under both eyes-open and eyes-closed conditions across three sessions. The intraclass correlation coefficients (ICCs) were employed to quantify the reliability of the intrinsic neural timescales. Our results showed that intrinsic neural timescales exhibited good reliability (ICC > 0.6) at the whole-brain level across different index types and eye states. Spatially, except for the right temporal region in the eyes-open condition, all other regions showed moderate-to-high ICCs. Over 60% of the electrodes demonstrated moderate-to-high intrinsic neural timescale ICCs under both eyes-open and eyes-closed conditions, with ACW-0 being more stable than ACW-50. Moreover, in the new dataset, the above results were consistently reproduced. The present study comprehensively assessed the reliability of intrinsic neural timescale under various conditions, providing robust evidence for their stability in neuroscience and psychiatry.

Neurophysiology studies propose that predictive coding is implemented via alpha/beta (8-30 Hz) rhythms preparing specific pathways to process predicted inputs. This leads to a state of relative inhibition, reducing feedforward gamma (40-90 Hz) rhythms and spiking for predictable inputs. This is called predictive routing model. It is unclear which circuit mechanisms implement this push-pull interaction between alpha/beta and gamma rhythms. To explore how predictive routing is implemented, we developed a self-supervised learning algorithm we call generalized Stochastic Delta Rule (gSDR). Development of this algorithm was necessary because manual tuning of parameters (frequently used in computational modeling) is inefficient to search through a non-linear parameter space that leads to emergence of neuronal rhythms. We used gSDR to train biophysical neural circuits and validated the algorithm on simple objectives. Then we applied gSDR to model observed neurophysiology. We asked the model to reproduce a shift from baseline oscillatory dynamics (~<20Hz) to stimulus induced gamma (~40-90Hz) dynamics recorded in the macaque visual cortex. This gamma oscillation during stimulation emerged by self-modulation of synaptic weights via gSDR. We further showed that the gamma-beta push-pull interactions implied by predictive routing could emerge via stochastic modulation of the local circuitry as well as top-down modulatory inputs to a network. To summarize, gSDR succeeded in training biophysical neural circuits to satisfy a series of neuronal objectives. This revealed the inhibitory neuron mechanisms underlying the gamma-beta push-pull dynamics that are observed during predictive processing tasks in systems and cognitive neuroscience.

Persisting symptoms and disability remain a problem for an appreciable proportion of people with schizophrenia despite treatment with antipsychotic medication. Improving outcomes requires an understanding of the nature and mechanisms of the pathological processes underlying persistence. Classical features of schizophrenia, which include disorganization and impoverishment of mental activity, are well-recognized early clinical features that predict poor long-term outcome. Substantial evidence indicates that these features reflect imprecise predictive coding. Predictive coding provides an overarching framework for understanding efficient functioning of the nervous system. Imprecise predictive coding also has the potential to precipitate acute psychosis characterized by reality distortion (delusions and hallucinations) at times of stress. On the other hand, substantial evidence indicates that persistent reality distortion itself gives rise to poor occupational and social function in the long term. Furthermore, abuse of psychotomimetic drugs, which exacerbate reality distortion, contributes to poor long-term outcome in schizophrenia. Neural circuits involved in modulating volitional acts are well understood to be implicated in addiction. Plastic changes in these circuits may account for the association between psychotomimetic drug abuse and poor outcomes in schizophrenia. We propose a mechanistic model according to which unbalanced inputs to the corpus striatum disturb the precision of subcortical modulation of cortical activity supporting volitional action. This model accounts for the evidence that early classical symptoms predict poor outcome, while in some circumstances, persistent reality distortion also predicts poor outcome. This model has implications for the development of novel treatments that address the risk of persisting symptoms and disabilities in schizophrenia.

Alterations of mismatch responses (ie, neural activity evoked by unexpected stimuli) are often considered a potential biomarker of schizophrenia. Going beyond establishing the type of observed alterations found in diagnosed patients and related cohorts, computational methods can yield valuable insights into the underlying disruptions of neural mechanisms and cognitive function. Here, we adopt a typology of model-based approaches from computational cognitive neuroscience, providing an overview of the study of mismatch responses and their alterations in schizophrenia from four complementary perspectives: (a) connectivity models, (b) decoding models, (c) neural network models, and (d) cognitive models. Connectivity models aim at inferring the effective connectivity patterns between brain regions that may underlie mismatch responses measured at the sensor level. Decoding models use multivariate spatiotemporal mismatch response patterns to infer the type of sensory violations or to classify participants based on their diagnosis. Neural network models such as deep convolutional neural networks can be used for improved classification performance as well as for a systematic study of various aspects of empirical data. Finally, cognitive models quantify mismatch responses in terms of signaling and updating perceptual predictions over time. In addition to describing the available methodology and reviewing the results of recent computational psychiatry studies, we offer suggestions for future work applying model-based techniques to advance the study of mismatch responses in schizophrenia.

Reduced glutamatergic excitability of the anterior cingulate cortex (ACC) has been long suspected in schizophrenia; recent observations support low glutamatergic tone as the primary pathophysiology contributing to subtle early features of this illness, with a secondary disinhibition (higher glutamate tone) resulting in more prominent clinical symptoms later in its course. We sought to investigate whether people with genetic high risk (GHR) for schizophrenia have lower glutamate levels in the ACC than those at later stages of clinical high risk (CHR) and those with first-episode schizophrenia (FES), among whom symptoms are already prominent.

We recruited people with CHR, GHR, or FES, as well as healthy controls. Using proton magnetic resonance spectroscopy, we determined glutamate levels in the perigenual ACC (pACC) and dorsal ACC (dACC) using a 3 T scanner.

We recruited 302 people across multiple stages of psychosis, including 63 with CHR, 76 with GHR, and 96 with FES, as well as 67 healthy controls. Those with GHR had lower glutamate levels in the dACC than those with CHR, while those with CHR had higher glutamate levels in the pACC than those with FES. Higher disorganization, but not any other symptom domain, was associated with lower levels of glutamate in the GHR group (dACC and pACC) and in the CHR group (pACC).

The cross-sectional design precluded inferences regarding individual clinical trajectory and resolution at 3 T was insufficient to separate spectra of glutamine from glutamate.

Reduced glutamatergic tone among people genetically predisposed to schizophrenia supports diminished excitability as an early feature of schizophrenia, contributing to the subtle symptom of disorganization across high-risk states. Higher glutamate levels become apparent when psychotic symptoms become prominent, possibly as a disinhibitory effect and, at the full-blown stage of psychosis, the relationship between glutamate concentrations and symptoms ceases to be simply linear.

In response to shortcomings of the current classification system in translating discoveries from basic science to clinical applications, NIMH offers a new framework for studying mental health disorders called Research Domain Criteria (RDoC). This framework holds a multidimensional outlook on psychopathologies focusing on functional domains of behavior and their implementing neural circuits. In parallel, the Predictive Processing (PP) framework stands as a leading theory of human brain function, offering a unified explanation for various types of information processing in the brain. While both frameworks share an interest in studying psychopathologies based on pathophysiology, their integration still needs to be explored. Here, we argued in favor of the explanatory power of PP to be a groundwork for the RDoC matrix in validating its constructs and creating testable hypotheses about mechanistic interactions between molecular biomarkers and clinical traits. Together, predictive processing may serve as a foundation for achieving the goals of the RDoC framework.

Mental illnesses arise from dysfunction in the brain. Although numerous extraneural factors influence these illnesses, ultimately, it is the science of the brain that will lead to novel therapies. Meanwhile, our understanding of this complex organ is incomplete, leading to the oft-repeated trope that neuroscience has yet to make significant contributions to the care of individuals with mental illnesses. This review seeks to counter this narrative, using specific examples of how neuroscientific advances have contributed to progress in mental health care in the past and how current achievements set the stage for further progress in the future.

Active inference describes (Bayes-optimal) behaviour as being motivated by the minimisation of surprise of one's sensory observations, through the optimisation of a generative model (of the hidden causes of one's sensory data) in the brain. One of active inference's key appeals is its conceptualisation of precision as biasing neuronal communication and, thus, inference within generative models. The importance of precision in perceptual inference is evident-many studies have demonstrated the importance of ensuring precision estimates are correct for normal (healthy) sensation and perception. Here, we highlight the many roles precision plays in action, i.e., the key processes that rely on adequate estimates of precision, from decision making and action planning to the initiation and control of muscle movement itself. Thereby, we focus on the recent development of hierarchical, "mixed" models-generative models spanning multiple levels of discrete and continuous inference. These kinds of models open up new perspectives on the unified description of hierarchical computation, and its implementation, in action. Here, we highlight how these models reflect the many roles of precision in action-from planning to execution-and the associated pathologies if precision estimation goes wrong. We also discuss the potential biological implementation of the associated message passing, focusing on the role of neuromodulatory systems in mediating different kinds of precision.

Magnetoencephalography (MEG) allows the non-invasive measurement of brain activity at millisecond precision combined with localization of the underlying generators. So far, MEG-systems consisted of superconducting quantum interference devices (SQUIDS), which suffer from several limitations. Recent technological advances, however, have enabled the development of novel MEG-systems based on optically pumped magnetometers (OPMs), offering several advantages over conventional SQUID-MEG systems. Considering potential improvements in the measurement of neuronal signals as well as reduced operating costs, the application of OPM-MEG systems for clinical neuroscience and diagnostic settings is highly promising. Here we provide an overview of the current state-of-the art of OPM-MEG and its unique potential for translational neuroscience. First, we discuss the technological features of OPMs and benchmark OPM-MEG against SQUID-MEG and electroencephalography (EEG), followed by a summary of pioneering studies of OPMs in healthy populations. Key applications of OPM-MEG for the investigation of psychiatric and neurological conditions are then reviewed. Specifically, we suggest novel applications of OPM-MEG for the identification of biomarkers and circuit deficits in schizophrenia, dementias, movement disorders, epilepsy, and neurodevelopmental syndromes (autism spectrum disorder and attention deficit hyperactivity disorder). Finally, we give an outlook of OPM-MEG for translational neuroscience with a focus on remaining methodological and technical challenges.

Patients with schizophrenia exhibit abnormalities in spatial frequency sensitivity, and it is believed that these abnormalities indicate more widespread dysfunction and dysregulation of bottom-up processing. The early visual system, including the first-order Lateral Geniculate Nucleus of the thalamus (LGN) and the primary visual cortex (V1), are key contributors to spatial frequency sensitivity. Medicated and unmedicated patients with schizophrenia exhibit contrasting changes in spatial frequency sensitivity, thus making it a useful probe for examining potential effects of the disorder and antipsychotic medications in neural processing. We constructed a parameterized, rate-based neural model of on-center/off-surround neurons in the early visual system to investigate the impacts of changes to the excitatory and inhibitory receptive field subfields. By incorporating changes in both the excitatory and inhibitory subfields that are associated with pathophysiological findings in schizophrenia, the model successfully replicated perceptual data from behavioral/functional studies involving medicated and unmedicated patients. Among several plausible mechanisms, our results highlight the dampening of excitation and/or increase in the spread and strength of the inhibitory subfield in medicated patients and the contrasting decreased spread and strength of inhibition in unmedicated patients. Given that the model was successful at replicating results from perceptual data under a variety of conditions, these elements of the receptive field may be useful markers for the imbalances seen in patients with schizophrenia.

Psychosis involves a distortion of thought content, which is partly reflected in anomalous ways in which words are semantically connected into utterances in speech. We sought to explore how these linguistic anomalies are realized through putative circuit-level abnormalities in the brain's semantic network.

Using a computational large-language model, Bidirectional Encoder Representations from Transformers (BERT), we quantified the contextual expectedness of a given word sequence (perplexity) across 180 samples obtained from descriptions of 3 pictures by patients with first-episode schizophrenia (FES) and controls matched for age, parental social status, and sex, scanned with 7 T ultra-high field functional magnetic resonance imaging (fMRI). Subsequently, perplexity was used to parametrize a spectral dynamic causal model (DCM) of the effective connectivity within (intrinsic) and between (extrinsic) 4 key regions of the semantic network at rest, namely the anterior temporal lobe, the inferior frontal gyrus (IFG), the posterior middle temporal gyrus (MTG), and the angular gyrus.

We included 60 participants, including 30 patients with FES and 30 controls. We observed higher perplexity in the FES group, indicating that speech was less predictable by the preceding context among patients. Results of Bayesian model comparisons showed that a DCM including the group by perplexity interaction best explained the underlying patterns of neural activity. We observed an increase of self-inhibitory effective connectivity within the IFG, as well as reduced self-inhibitory tone within the pMTG, in the FES group. An increase in self-inhibitory tone in the IFG correlated strongly and positively with inter-regional excitation between the IFG and posterior MTG, while self-inhibition of the posterior MTG was negatively correlated with this interregional excitation.

Our design did not address connectivity in the semantic network during tasks that selectively activated the semantic network, which could corroborate findings from this resting-state fMRI study. Furthermore, we do not present a replication study, which would ideally use speech in a different language.

As an explanation for peculiar speech in psychosis, these results index a shift in the excitatory-inhibitory balance regulating information flow across the semantic network, confined to 2 regions that were previously linked specifically to the executive control of meaning. Based on our approach of combining a large language model with causal connectivity estimates, we propose loss in semantic control as a potential neurocognitive mechanism contributing to disorganization in psychosis.

Robust epidemiological evidence of risk and protective factors for psychosis is essential to inform preventive interventions. Previous evidence syntheses have classified these risk and protective factors according to their strength of association with psychosis. In this critical review we appraise the distinct and overlapping mechanisms of 25 key environmental risk factors for psychosis, and link these to mechanistic pathways that may contribute to neurochemical alterations hypothesised to underlie psychotic symptoms. We then discuss the implications of our findings for future research, specifically considering interactions between factors, exploring universal and subgroup-specific factors, improving understanding of temporality and risk dynamics, standardising operationalisation and measurement of risk and protective factors, and developing preventive interventions targeting risk and protective factors.

Schizophrenia (SZ) is a complex neuropsychiatric disorder associated with severe cognitive dysfunction. Although research has mainly focused on forebrain abnormalities, emerging results support the involvement of the cerebellum in SZ physiopathology, particularly in Cognitive Impairment Associated with SZ (CIAS). Besides its role in motor learning and control, the cerebellum is implicated in cognition and emotion. Recent research suggests that structural and functional changes in the cerebellum are linked to deficits in various cognitive domains including attention, working memory, and decision-making. Moreover, cerebellar dysfunction is related to altered cerebellar circuit activities and connectivity with brain regions associated with cognitive processing. This review delves into the role of the cerebellum in CIAS. We initially consider the major forebrain alterations in CIAS, addressing impairments in neurotransmitter systems, synaptic plasticity, and connectivity. We then focus on recent findings showing that several mechanisms are also altered in the cerebellum and that cerebellar communication with the forebrain is impaired. This evidence implicates the cerebellum as a key component of circuits underpinning CIAS physiopathology. Further studies addressing cerebellar involvement in SZ and CIAS are warranted and might open new perspectives toward understanding the physiopathology and effective treatment of these disorders.

Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.

The canonical microcircuit (CMC) has been hypothesized to be the fundamental unit of information processing in cortex. Each CMC unit is thought to be an interconnected column of neurons with specific connections between excitatory and inhibitory neurons across layers. Recently, we identified a conserved spectrolaminar motif of oscillatory activity across the primate cortex that may be the physiological consequence of the CMC. The spectrolaminar motif consists of local field potential (LFP) gamma-band power (40-150 Hz) peaking in superficial layers 2 and 3 and alpha/beta-band power (8-30 Hz) peaking in deep layers 5 and 6. Here, we investigate whether specific conserved cell types may produce the spectrolaminar motif. We collected laminar histological and electrophysiological data in 11 distinct cortical areas spanning the visual hierarchy: V1, V2, V3, V4, TEO, MT, MST, LIP, 8A/FEF, PMD, and LPFC (area 46), and anatomical data in DP and 7A. We stained representative slices for the three main inhibitory subtypes, Parvalbumin (PV), Calbindin (CB), and Calretinin (CR) positive neurons, as well as pyramidal cells marked with Neurogranin (NRGN). We found a conserved laminar structure of PV, CB, CR, and pyramidal cells. We also found a consistent relationship between the laminar distribution of inhibitory subtypes with power in the local field potential. PV interneuron density positively correlated with gamma (40-150 Hz) power. CR and CB density negatively correlated with alpha (8-12 Hz) and beta (13-30 Hz) oscillations. The conserved, layer-specific pattern of inhibition and excitation across layers is therefore likely the anatomical substrate of the spectrolaminar motif.

Neuronal oscillations emerge as an interplay between excitatory and inhibitory neurons and underlie cognitive functions and conscious states. These oscillations have distinct expression patterns across cortical layers. Does cellular anatomy enable these oscillations to emerge in specific cortical layers? We present a comprehensive analysis of the laminar distribution of the three main inhibitory cell types in primate cortex (Parvalbumin, Calbindin, and Calretinin positive) and excitatory pyramidal cells. We found a canonical relationship between the laminar anatomy and electrophysiology in 11 distinct primate areas spanning from primary visual to prefrontal cortex. The laminar anatomy explained the expression patterns of neuronal oscillations in different frequencies. Our work provides insight into the cortex-wide cellular mechanisms that generate neuronal oscillations in primates.

Introduction: The Adverse Outcome Pathway (AOP) concept facilitates rapid hazard assessment for human health risks. AOPs are constantly evolving, their number is growing, and they are referenced in the AOP-Wiki database, which is supported by the OECD. Here, we present a study that aims at identifying well-defined biological areas, as well as gaps within the AOP-Wiki for future research needs. It does not intend to provide a systematic and comprehensive summary of the available literature on AOPs but summarizes and maps biological knowledge and diseases represented by the already developed AOPs (with OECD endorsed status or under validation). Methods: Knowledge from the AOP-Wiki database were extracted and prepared for analysis using a multi-step procedure. An automatic mapping of the existing information on AOPs (i.e., genes/proteins and diseases) was performed using bioinformatics tools (i.e., overrepresentation analysis using Gene Ontology and DisGeNET), allowing both the classification of AOPs and the development of AOP networks (AOPN). Results: AOPs related to diseases of the genitourinary system, neoplasms and developmental anomalies are the most frequently investigated on the AOP-Wiki. An evaluation of the three priority cases (i.e., immunotoxicity and non-genotoxic carcinogenesis, endocrine and metabolic disruption, and developmental and adult neurotoxicity) of the EU-funded PARC project (Partnership for the Risk Assessment of Chemicals) are presented. These were used to highlight under- and over-represented adverse outcomes and to identify and prioritize gaps for further research. Discussion: These results contribute to a more comprehensive understanding of the adverse effects associated with the molecular events in AOPs, and aid in refining risk assessment for stressors and mitigation strategies. Moreover, the FAIRness (i.e., data which meets principles of findability, accessibility, interoperability, and reusability (FAIR)) of the AOPs appears to be an important consideration for further development.

A fundamental property of sensory systems is their ability to detect novel stimuli in the ambient environment. The auditory brain contains neurons that decrease their response to repetitive sounds but increase their firing rate to novel or deviant stimuli; the difference between both responses is known as stimulus-specific adaptation or neuronal mismatch (nMM). Here, we tested the effect of microiontophoretic applications of ACh on the neuronal responses in the auditory cortex (AC) of anesthetized rats during an auditory oddball paradigm, including cascade controls. Results indicate that ACh modulates the nMM, affecting prediction error responses but not repetition suppression, and this effect is manifested predominantly in infragranular cortical layers. The differential effect of ACh on responses to standards, relative to deviants (in terms of averages and variances), was consistent with the representational sharpening that accompanies an increase in the precision of prediction errors. These findings suggest that ACh plays an important role in modulating prediction error signaling in the AC and gating the access of these signals to higher cognitive levels.

Individuals with schizophrenia or bipolar disorder have attenuated auditory mismatch negativity (MMN) responses, indicating impaired sensory information processing. Computational models of effective connectivity between brain areas underlying MMN responses show reduced connectivity between fronto-temporal areas in individuals with schizophrenia. Here we ask whether children at familial high risk (FHR) of developing a serious mental disorder show similar alterations.

We recruited 67 children at FHR for schizophrenia, 47 children at FHR for bipolar disorder as well as 59 matched population-based controls from the Danish High Risk and Resilience study. The 11-12-year-old participants engaged in a classical auditory MMN paradigm with deviations in frequency, duration, or frequency and duration, while we recorded their EEG. We used dynamic causal modeling (DCM) to infer on the effective connectivity between brain areas underlying MMN.

DCM yielded strong evidence for differences in effective connectivity among groups in connections from right inferior frontal gyrus (IFG) to right superior temporal gyrus (STG), along with differences in intrinsic connectivity within primary auditory cortex (A1). Critically, the 2 high-risk groups differed in intrinsic connectivity in left STG and IFG as well as effective connectivity from right A1 to right STG. Results persisted even when controlling for past or present psychiatric diagnoses.

We provide novel evidence that connectivity underlying MMN responses in children at FHR for schizophrenia and bipolar disorder is altered at the age of 11-12, echoing findings that have been found in individuals with manifest schizophrenia.

Patients with schizophrenia exhibit abnormalities in spatial frequency sensitivity, and it is believed that these abnormalities indicate more widespread dysfunction and dysregulation of bottom-up processing. The early visual system, including the first-order Lateral Geniculate Nucleus of the thalamus (LGN) and the primary visual cortex (V1), are key contributors to spatial frequency sensitivity. Medicated and unmedicated patients with schizophrenia exhibit contrasting changes in spatial frequency sensitivity, thus making it a useful probe for examining potential effects of the disorder and antipsychotic medications in neural processing. We constructed a parameterized, rate-based neural model of on-center/off-surround neurons in the early visual system to investigate the impacts of changes to the excitatory and inhibitory receptive field subfields. By incorporating changes in both the excitatory and inhibitory subfields that are associated with pathophysiological findings in schizophrenia, the model successfully replicated perceptual data from behavioral/functional studies involving medicated and unmedicated patients. Among several plausible mechanisms, our results highlight the dampening of excitation and/or increase in the spread and strength of the inhibitory subfield in medicated patients and the contrasting decreased spread and strength of inhibition in unmedicated patients. Given that the model was successful at replicating results from perceptual data under a variety of conditions, these elements of the receptive field may be useful markers for the imbalances seen in patients with schizophrenia.

Electroencephalography studies link sensory processing issues in schizophrenia to increased noise level-noise here is background spontaneous activity-as measured by the signal-to-noise ratio. The mechanism, however, of such increased noise is unknown. We investigate if this relates to changes in cortical excitation-inhibition balance, which has been observed to be atypical in schizophrenia, by combining electroencephalography and computational modeling. Our electroencephalography task results, for which the local field potentials can be used as a proxy, show lower signal-to-noise ratio due to higher noise in schizophrenia. Both electroencephalography rest and task states exhibit higher levels of excitation in the functional excitation-inhibition (as a proxy of excitation-inhibition balance). This suggests a relationship between increased noise and atypical excitation in schizophrenia, which was addressed by using computational modeling. A Leaky Integrate-and-Fire model was used to simulate the effects of varying degrees of noise on excitation-inhibition balance, local field potential, NMDA current, and . Results show a noise-related increase in the local field potential, excitation in excitation-inhibition balance, pyramidal NMDA current, and spike rate. Mutual information and mediation analysis were used to explore a cross-level relationship, showing that the cortical local field potential plays a key role in transferring the effect of noise to the cellular population level of NMDA.

There is converging evidence that 40-Hz auditory steady-state responses (ASSRs) are robustly impaired in schizophrenia and could constitute a potential biomarker for characterizing circuit dysfunctions as well as enable early detection and diagnosis. Here, we provide an overview of the mechanisms involved in 40-Hz ASSRs, drawing on computational, physiological, and pharmacological data with a focus on parameters modulating the balance between excitation and inhibition. We will then summarize findings from electro- and magnetoencephalographic studies in participants at clinical high risk for psychosis, patients with first-episode psychosis, and patients with schizophrenia to identify the pattern of deficits across illness stages, the relationship with clinical variables, and the prognostic potential. Finally, data on genetics and developmental modifications will be reviewed, highlighting the importance of late modifications of 40-Hz ASSRs during adolescence, which are closely related to the underlying changes in GABA (gamma-aminobutyric acid) interneurons. Together, our review suggests that 40-Hz ASSRs may constitute an informative electrophysiological approach to characterize circuit dysfunctions in psychosis that could be relevant for the development of mechanistic biomarkers.

Disrupted language in psychotic disorders, such as schizophrenia, can manifest as false contents and formal deviations, often described as thought disorder. These features play a critical role in the social dysfunction associated with psychosis, but we continue to lack insights regarding how and why these symptoms develop. Natural language generation (NLG) is a field of computer science that focuses on generating human-like language for various applications. The theory that psychosis is related to the evolution of language in humans suggests that NLG systems that are sufficiently evolved to generate human-like language may also exhibit psychosis-like features. In this conceptual review, we propose using NLG systems that are at various stages of development as in silico tools to study linguistic features of psychosis. We argue that a program of in silico experimental research on the network architecture, function, learning rules, and training of NLG systems can help us understand better why thought disorder occurs in patients. This will allow us to gain a better understanding of the relationship between language and psychosis and potentially pave the way for new therapeutic approaches to address this vexing challenge.

Extracting regularities from ongoing stimulus streams to form predictions is crucial for adaptive behavior. Such regularities exist in terms of the content of the stimuli and their timing, both of which are known to interactively modulate sensory processing. In real-world stimulus streams such as music, regularities can occur at multiple levels, both in terms of contents (e.g., predictions relating to individual notes vs. their more complex groups) and timing (e.g., pertaining to timing between intervals vs. the overall beat of a musical phrase). However, it is unknown whether the brain integrates predictions in a manner that is mutually congruent (e.g., if "beat" timing predictions selectively interact with "what" predictions falling on pulses which define the beat), and whether integrating predictions in different timing conditions relies on dissociable neural correlates.

To address these questions, our study manipulated "what" and "when" predictions at different levels - (local) interval-defining and (global) beat-defining - within the same stimulus stream, while neural activity was recorded using electroencephalogram (EEG) in participants (N = 20) performing a repetition detection task.

Our results reveal that temporal predictions based on beat or interval timing modulated mismatch responses to violations of "what" predictions happening at the predicted time points, and that these modulations were shared between types of temporal predictions in terms of the spatiotemporal distribution of EEG signals. Effective connectivity analysis using dynamic causal modeling showed that the integration of "what" and "when" predictions selectively increased connectivity at relatively late cortical processing stages, between the superior temporal gyrus and the fronto-parietal network.

Taken together, these results suggest that the brain integrates different predictions with a high degree of mutual congruence, but in a shared and distributed cortical network. This finding contrasts with recent studies indicating separable mechanisms for beat-based and memory-based predictive processing.

A central feature of schizophrenia is the disorganization and impoverishment of language. Recently, we observed higher semantic similarity in first-episode-schizophrenia (FES) patients. In this study, we investigate if this aberrant similarity relates to the 'causal' connectivity between two key nodes of the word production system: inferior frontal gyrus (IFG) and the semantic-hub at the ventral anterior temporal lobe (vATL).

Resting-state fMRI scans were collected from 60 participants (30 untreated FES and 30 healthy controls). The semantic distance was measured with the CoVec semantic tool based on GloVe. A spectral dynamic causal model with Parametrical Empirical Bayes was constructed modelling the intrinsic self-inhibitory and extrinsic-excitatory connections within the brain regions. We estimated the parameters of a fully connected model with the semantic distance as a covariate.

FES patients chose words with higher semantic similarity when describing the pictures compared to the HC group. Among patients, an increased semantic similarity was related with an increase in intrinsic connections within both the vATL and IFG, suggesting that reduced 'synaptic gain' in these regions likely contribute to aberrant sampling of the semantic space during discourse in schizophrenia.

Lexical impoverishment relates to increased self-inhibition in both the IFG and vATL. The associated reduction in synaptic gain may relate to reduced precision of locally generated neural activity, forcing the choice of words that are already 'activated' in a lexical network. One approach to improve word sampling may be via promoting synaptic gain via supra-physiological stimulation within the Broca's-vATL network; this proposal needs verification.

The brain potential known as mismatch negativity (MMN) is one of the most studied indices of altered brain function in schizophrenia. This review looks at what has been learned about MMN in schizophrenia over the last three decades and why the level of interest and activity in this field of research remains strong. A diligent consideration of available evidence suggests that MMN can serve as a biomarker in schizophrenia, but perhaps not the kind of biomarker that early research supposed. This review concludes that MMN measurement is likely to be most useful as a monitoring and response biomarker enabling tracking of an underlying pathology and efficacy of interventions, respectively. The role of, and challenges presented by, pre-clinical models is discussed as well as the merits of different methodologies that can be brought to bear in pursuing a deeper understanding of pathophysiology that might explain smaller MMN in schizophrenia.

Language-related symptoms, such as disorganized, impoverished speech and communicative behaviors, are one of the core features of schizophrenia. These features most strongly correlate with cognitive deficits and polygenic risk among various symptom dimensions of schizophrenia. Nevertheless, unaffected siblings with genetic high-risk fail to show consistent deficits in language network (LN), indicating that either (1) polygenic risk has no notable effect on LN and/or (2) siblings show compensatory changes in opposing direction to patients. To answer this question, we related polygenic risk scores (PRS) to the region-level, tract-level, and systems-level structure (cortical thickness and fiber connectivity) of LN in 182 patients, 48 unaffected siblings and 135 healthy controls. We also studied the relationships between symptoms, language-related cognition, social functioning and LN structure. We observed a significantly lower thickness in LN (especially the Broca's, Wernicke's area and their right homologues) in patients. Siblings had a distinctly higher thickness in parts of the LN and a more pronounced small-world-like structural integration within the LN. Patients with reduced LN thickness had higher PRS, more disorganization and impoverished speech with lower language-related cognition and social functioning. We conclude that the genetic susceptibility and putative compensatory changes for schizophrenia operate, in part, via key regions in the Language Network.

Schizophrenia is a chronic disease presented with psychotic symptoms, negative symptoms, impairment in the reward system, and widespread neurocognitive deterioration. Disruption of synaptic connections in neural circuits is responsible for the disease's development and progression. Because deterioration in synaptic connections results in the impaired effective processing of information. Although structural impairments of the synapse, such as a decrease in dendritic spine density, have been shown in previous studies, functional impairments have also been revealed with the development of genetic and molecular analysis methods. In addition to abnormalities in protein complexes regulating exocytosis in the presynaptic region and impaired vesicle release, especially, changes in proteins related to postsynaptic signaling have been reported. In particular, impairments in postsynaptic density elements, glutamate receptors, and ion channels have been shown. At the same time, effects on cellular adhesion molecular structures such as neurexin, neuroligin, and cadherin family proteins were detected. Of course, the confusing effect of antipsychotic use in schizophrenia research should also be considered. Although antipsychotics have positive and negative effects on synapses, studies indicate synaptic deterioration in schizophrenia independent of drug use. In this review, the deterioration in synapse structure and function and the effects of antipsychotics on the synapse in schizophrenia will be discussed.

The hierarchical characteristics of the brain are prominent in the pharmacological treatment of psychiatric diseases, primarily targeting cellular receptors that extend upward to intrinsic connectivity within a region, interregional connectivity, and, consequently, clinical observations such as an electroencephalogram (EEG). To understand the long-term effects of neuropharmacological intervention on neurobiological properties at different hierarchical levels, we explored long-term changes in neurobiological parameters of an N-methyl-D-aspartate canonical microcircuit model (CMM-NMDA) in the default mode network (DMN) and auditory hallucination network (AHN) using dynamic causal modeling of longitudinal EEG in clozapine-treated patients with schizophrenia. The neurobiological properties of the CMM-NMDA model associated with symptom improvement in schizophrenia were found across hierarchical levels, from a reduced membrane capacity of the deep pyramidal cell and intrinsic connectivity with the inhibitory population in DMN and intrinsic and extrinsic connectivity in AHN. The medication duration mainly affects the intrinsic connectivity and NMDA time constant in DMN. Virtual perturbation analysis specified the contribution of each parameter to the cross-spectral density (CSD) of the EEG, particularly intrinsic connectivity and membrane capacitances for CSD frequency shifts and progression. It further reveals that excitatory and inhibitory connectivity complements frequency-specific CSD changes, notably the alpha frequency band in DMN. Positive and negative synergistic interactions exist between neurobiological properties primarily within the same region in patients treated with clozapine. The current study shows how computational neuropharmacology helps explore the multiscale link between neurobiological properties and clinical observations and understand the long-term mechanism of neuropharmacological intervention reflected in clinical EEG.

Cognitive deficits are a core feature of schizophrenia, account for much of the impaired functioning associated with the disorder and are not responsive to existing treatments. In this review, we first describe the clinical presentation and natural history of these deficits. We then consider aetiological factors, highlighting how a range of similar genetic and environmental factors are associated with both cognitive function and schizophrenia. We then review the pathophysiological mechanisms thought to underlie cognitive symptoms, including the role of dopamine, cholinergic signalling and the balance between GABAergic interneurons and glutamatergic pyramidal cells. Finally, we review the clinical management of cognitive impairments and candidate novel treatments.

Aberrant patterns of cognition, perception, and behavior seen in psychiatric disorders are thought to be driven by a complex interplay of neural processes that evolve at a rapid temporal scale. Understanding these dynamic processes in vivo in humans has been hampered by a trade-off between spatial and temporal resolutions inherent to current neuroimaging technology. A recent trend in psychiatric research has been the use of high temporal resolution imaging, particularly magnetoencephalography, often in conjunction with sophisticated machine learning decoding techniques. Developments here promise novel insights into the spatiotemporal dynamics of cognitive phenomena, including domains relevant to psychiatric illnesses such as reward and avoidance learning, memory, and planning. This review considers recent advances afforded by exploiting this increased spatiotemporal precision, with specific reference to applications that seek to drive a mechanistic understanding of psychopathology and the realization of preclinical translation.

Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM) that represent the mean activity of large numbers of neurons. Here, we provide first a new framework called laminar NMM, or LaNMM for short, where we combine conduction physics with NMMs to simulate electrophysiological measurements. Then, we employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. We define a minimal model capable of generating coupled slow and fast oscillations, and we optimize LaNMM-specific parameters to fit multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology by selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. In closing, we discuss how this hybrid modeling framework can be more generally used to infer cortical circuitry.

Active inference has become an influential concept in psychopathology. We apply active inference to investigate conceptual disorganization in first-episode schizophrenia. We conceptualize speech production as a decision-making process affected by the latent "conceptual organization"-as a special case of uncertainty about the causes of sensory information. Uncertainty is both minimized via speech production-in which function words index conceptual organization in terms of analytic thinking-and tracked by a domain-general salience network. We hypothesize that analytic thinking depends on conceptual organization. Therefore, conceptual disorganization in schizophrenia would be both indexed by low conceptual organization and reflected in the effective connectivity within the salience network.

With 1-minute speech samples from a picture description task and resting state fMRI from 30 patients and 30 healthy subjects, we employed dynamic causal and probabilistic graphical models to investigate if the effective connectivity of the salience network underwrites conceptual organization.

Low analytic thinking scores index low conceptual organization which affects diagnostic status. The influence of the anterior insula on the anterior cingulate cortex and the self-inhibition within the anterior cingulate cortex are elevated given low conceptual organization (ie, conceptual disorganization).

Conceptual organization, a construct that explains formal thought disorder, can be modeled in an active inference framework and studied in relation to putative neural substrates of disrupted language in schizophrenia. This provides a critical advance to move away from rating-scale scores to deeper constructs in the pursuit of the pathophysiology of formal thought disorder.

The mixed cognitive outcomes in early psychosis (EP) have important implications for recovery. In this longitudinal study, we asked whether baseline differences in the cognitive control system (CCS) in EP participants would revert toward a normative trajectory seen in healthy controls (HC). Thirty EP and 30 HC undertook functional MRI at baseline using the multi-source interference task-a paradigm that selectively introduces stimulus conflict-and 19 in each group repeated the task at 12 months. Activation of the left superior parietal cortex normalized over time for the EP group, relative to HC, coincident with improvements in reaction time and social-occupational functioning. To examine these group and timepoint differences, we used dynamic causal modeling to infer changes in effective connectivity between regions underlying the MSIT task execution, namely visual, anterior insula, anterior cingulate, and superior parietal cortical regions. To resolve stimulus conflict, EP participants transitioned from an indirect to a direct neuromodulation of sensory input to the anterior insula over timepoints, though not as strongly as HC participants. Stronger direct nonlinear modulation of the anterior insula by the superior parietal cortex at follow-up was associated with improved task performance. Overall, normalization of the CCS through adoption of more direct processing of complex sensory input to the anterior insula, was observed in EP after 12 months of treatment. Such processing of complex sensory input reflects a computational principle called gain control, which appears to track changes in cognitive trajectory within the EP group.

Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.

This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain-from cognitive and computational neuroscience-as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we-or our brains-encode uncertainty or its complement, precision. It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms.

Emerging research on neuroplasticity processes in psychosis spectrum illnesses-from the synaptic to the macrocircuit levels-fill key gaps in our models of pathophysiology and open up important treatment considerations. In this selective narrative review, we focus on three themes, emphasizing alterations in spike-timing dependent and Hebbian plasticity that occur during adolescence, the critical period for prefrontal system development: (1) Experience-dependent dysplasticity in psychosis emerges from activity decorrelation within neuronal ensembles. (2) Plasticity processes operate bidirectionally: deleterious environmental and experiential inputs shape microcircuits. (3) Dysregulated plasticity processes interact across levels of scale and time and include compensatory mechanisms that have pathogenic importance. We present evidence that-given the centrality of progressive dysplastic changes, especially in prefrontal cortex-pharmacologic or neuromodulatory interventions will need to be supplemented by corrective learning experiences for the brain if we are to help people living with these illnesses to fully thrive.

Individuals with schizophrenia (SZ) exhibit cognitive performance below expected levels based on familial cognitive aptitude. One such cognitive process, working memory (WM), is robustly impaired in SZ. These WM impairments, which emerge over development during the premorbid and prodromal stages of SZ, appear to reflect alterations in the neural circuitry of the dorsolateral prefrontal cortex. Within the dorsolateral prefrontal cortex, a microcircuit formed by reciprocal connections between excitatory layer 3 pyramidal neurons and inhibitory parvalbumin basket cells (PVBCs) appears to be a key neural substrate for WM. Postmortem human studies indicate that both layer 3 pyramidal neurons and PVBCs are altered in SZ, suggesting that levels of excitation and inhibition are lower in the microcircuit. Studies in monkeys indicate that features of both cell types exhibit distinctive postnatal developmental trajectories. Together, the results of these studies suggest a model in which 1) genetic and/or early environmental insults to excitatory signaling in layer 3 pyramidal neurons give rise to cognitive impairments during the prodromal phase of SZ and evoke compensatory changes in inhibition that alter the developmental trajectories of PVBCs, and 2) synaptic pruning during adolescence further lowers excitatory activity to a level that exceeds the compensatory capacity of PVBC inhibition, leading to a failure of the normal maturational improvements in WM during the prodromal and early clinical stages of SZ. Findings that support as well as challenge this model are discussed.