Computational Modeling of Electroencephalography and Functional Magnetic Resonance Imaging Paradigms Indicates a Consistent Loss of Pyramidal Cell Synaptic Gain in Schizophrenia

Exploring causal mechanisms of psychosis risk

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.

New clues for the role of cerebellum in schizophrenia and the associated cognitive impairment

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: from basic neuroscience to clinical use

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 laminar organization of cell types in macaque cortex and its relationship to neuronal oscillations

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.

Significance Statement

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.

Comprehensive mapping of the AOP-Wiki database: identifying biological and disease gaps

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.

Acetylcholine modulates the precision of prediction error in the auditory cortex

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.

Children at Familial High risk of Schizophrenia and Bipolar Disorder Exhibit Altered Connectivity Patterns During Pre-attentive Processing of an Auditory Prediction Error

BACKGROUND AND HYPOTHESIS

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.

STUDY DESIGN

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.

STUDY RESULTS

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.

CONCLUSIONS

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.

A neural modeling approach to study mechanisms underlying the heterogeneity of visual spatial frequency sensitivity in 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.

Increased noise relates to abnormal excitation-inhibition balance in schizophrenia: a combined empirical and computational study

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.

40-Hz Auditory Steady-State Responses in Schizophrenia: Toward a Mechanistic Biomarker for Circuit Dysfunctions and Early Detection and Diagnosis

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.

Studying Psychosis Using Natural Language Generation: A Review of Emerging Opportunities

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.

"What" and "when" predictions modulate auditory processing in a mutually congruent manner

Introduction

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.

Methods

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.

Results

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.

Discussion

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.

Language network self-inhibition and semantic similarity in first-episode schizophrenia: A computational-linguistic and effective connectivity approach

INTRODUCTION

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).

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Why mismatch negativity continues to hold potential in probing altered brain function in schizophrenia

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.

Polygenic risk for schizophrenia and the language network: Putative compensatory reorganization in unaffected siblings

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.

Synaptic dysfunction in schizophrenia

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.

Neuropharmacological computational analysis of longitudinal electroencephalograms in clozapine-treated patients with schizophrenia using hierarchical dynamic causal modeling

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 impairment in schizophrenia: aetiology, pathophysiology, and treatment

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.

Artificial intelligence ecosystem for computational psychiatry: Ideas to practice

Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms. One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable. This may help researchers develop more effective treatments and interventions for mental health problems. This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry. The artificial intelligence ecosystem for computational psychiatry includes data acquisition, preparation, modeling, application, and evaluation. This approach allows researchers to integrate data from a variety of sources, such as brain imaging, genetics, and behavioral experiments, to obtain a more complete understanding of mental health conditions. Through the process of data preprocessing, training, and testing, the data that are required for model building can be prepared. By using machine learning, neural networks, artificial intelligence, and other methods, researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors. Despite the continuous development and breakthrough of computational psychiatry, it has not yet influenced routine clinical practice and still faces many challenges, such as data availability and quality, biological risks, equity, and data protection. As we move progress in this field, it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.

Spatiotemporal Precision of Neuroimaging in Psychiatry

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.

A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings

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, Epistemic Value, and Uncertainty in Conceptual Disorganization in First-Episode Schizophrenia

BACKGROUND AND HYPOTHESIS

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.

STUDY DESIGN

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.

STUDY RESULTS

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).

CONCLUSIONS

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.

Longitudinal changes in neural gain and its relationship to cognitive control trajectory in young adults with early psychosis

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.

Understanding mental health through computers: An introduction to computational psychiatry

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.

Computational psychiatry: from synapses to sentience

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.

Psychosis spectrum illnesses as disorders of prefrontal critical period plasticity

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.

The promise of a model-based psychiatry: building computational models of mental ill health

Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.

Cognitive Dysfunction and Prefrontal Cortical Circuit Alterations in Schizophrenia: Developmental Trajectories

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.

Integrating the Neurodevelopmental and Dopamine Hypotheses of Schizophrenia and the Role of Cortical Excitation-Inhibition Balance

The neurodevelopmental and dopamine hypotheses are leading theories of the pathoetiology of schizophrenia, but they were developed in isolation. However, since they were originally proposed, there have been considerable advances in our understanding of the normal neurodevelopmental refinement of synapses and cortical excitation-inhibition (E/I) balance, as well as preclinical findings on the interrelationship between cortical and subcortical systems and new in vivo imaging and induced pluripotent stem cell evidence for lower synaptic density markers in patients with schizophrenia. Genetic advances show that schizophrenia is associated with variants linked to genes affecting GABA (gamma-aminobutyric acid) and glutamatergic signaling as well as neurodevelopmental processes. Moreover, in vivo studies on the effects of stress, particularly during later development, show that it leads to synaptic elimination. We review these lines of evidence as well as in vivo evidence for altered cortical E/I balance and dopaminergic dysfunction in schizophrenia. We discuss mechanisms through which frontal cortex circuitry may regulate striatal dopamine and consider how frontal E/I imbalance may cause dopaminergic dysregulation to result in psychotic symptoms. This integrated neurodevelopmental and dopamine hypothesis suggests that overpruning of synapses, potentially including glutamatergic inputs onto frontal cortical interneurons, disrupts the E/I balance and thus underlies cognitive and negative symptoms. It could also lead to disinhibition of excitatory projections from the frontal cortex and possibly other regions that regulate mesostriatal dopamine neurons, resulting in dopamine dysregulation and psychotic symptoms. Together, this explains a number of aspects of the epidemiology and clinical presentation of schizophrenia and identifies new targets for treatment and prevention.

The neurophysiological effect of NMDA-R antagonism of frontotemporal lobar degeneration is conditional on individual GABA concentration

There is a pressing need to accelerate therapeutic strategies against the syndromes caused by frontotemporal lobar degeneration, including symptomatic treatments. One approach is for experimental medicine, coupling neurophysiological studies of the mechanisms of disease with pharmacological interventions aimed at restoring neurochemical deficits. Here we consider the role of glutamatergic deficits and their potential as targets for treatment. We performed a double-blind placebo-controlled crossover pharmaco-magnetoencephalography study in 20 people with symptomatic frontotemporal lobar degeneration (10 behavioural variant frontotemporal dementia, 10 progressive supranuclear palsy) and 19 healthy age- and gender-matched controls. Both magnetoencephalography sessions recorded a roving auditory oddball paradigm: on placebo or following 10 mg memantine, an uncompetitive NMDA-receptor antagonist. Ultra-high-field magnetic resonance spectroscopy confirmed lower concentrations of GABA in the right inferior frontal gyrus of people with frontotemporal lobar degeneration. While memantine showed a subtle effect on early-auditory processing in patients, there was no significant main effect of memantine on the magnitude of the mismatch negativity (MMN) response in the right frontotemporal cortex in patients or controls. However, the change in the right auditory cortex MMN response to memantine (vs. placebo) in patients correlated with individuals' prefrontal GABA concentration. There was no moderating effect of glutamate concentration or cortical atrophy. This proof-of-concept study demonstrates the potential for baseline dependency in the pharmacological restoration of neurotransmitter deficits to influence cognitive neurophysiology in neurodegenerative disease. With changes to multiple neurotransmitters in frontotemporal lobar degeneration, we suggest that individuals' balance of excitation and inhibition may determine drug efficacy, with implications for drug selection and patient stratification in future clinical trials.

Functional neuroimaging in psychiatry and the case for failing better

Psychiatric disorders encompass complex aberrations of cognition and affect and are among the most debilitating and poorly understood of any medical condition. Current treatments rely primarily on interventions that target brain function (drugs) or learning processes (psychotherapy). A mechanistic understanding of how these interventions mediate their therapeutic effects remains elusive. From the early 1990s, non-invasive functional neuroimaging, coupled with parallel developments in the cognitive neurosciences, seemed to signal a new era of neurobiologically grounded diagnosis and treatment in psychiatry. Yet, despite three decades of intense neuroimaging research, we still lack a neurobiological account for any psychiatric condition. Likewise, functional neuroimaging plays no role in clinical decision making. Here, we offer a critical commentary on this impasse and suggest how the field might fare better and deliver impactful neurobiological insights.

Emerging evidence for astrocyte dysfunction in schizophrenia

Schizophrenia is a complex, chronic mental health disorder whose heterogeneous genetic and neurobiological background influences early brain development, and whose precise etiology is still poorly understood. Schizophrenia is not characterized by gross brain pathology, but involves subtle pathological changes in neuronal populations and glial cells. Among the latter, astrocytes critically contribute to the regulation of early neurodevelopmental processes, and any dysfunctions in their morphological and functional maturation may lead to aberrant neurodevelopmental processes involved in the pathogenesis of schizophrenia, such as mitochondrial biogenesis, synaptogenesis, and glutamatergic and dopaminergic transmission. Studies of the mechanisms regulating astrocyte maturation may therefore improve our understanding of the cellular and molecular mechanisms underlying the pathogenesis of schizophrenia.

In silico Effects of Synaptic Connections in the Visual Thalamocortical Pathway

We have studied brain connectivity using a biologically inspired in silico model of the visual pathway consisting of the lateral geniculate nucleus (LGN) of the thalamus, and layers 4 and 6 of the primary visual cortex. The connectivity parameters in the model are informed by the existing anatomical parameters from mammals and rodents. In the base state, the LGN and layer 6 populations in the model oscillate with dominant alpha frequency, while the layer 4 oscillates in the theta band. By changing intra-cortical hyperparameters, specifically inhibition from layer 6 to layer 4, we demonstrate a transition to alpha mode for all the populations. Furthermore, by increasing the feedforward connectivities in the thalamo-cortico-thalamic loop, we could transition into the beta band for all the populations. On looking closely, we observed that the origin of this beta band is in the layer 6 (infragranular layers); lesioning the thalamic feedback from layer 6 removed the beta from the LGN and the layer 4. This agrees with existing physiological studies where it is shown that beta rhythm is generated in the infragranular layers. Lastly, we present a case study to demonstrate a neurological condition in the model. By changing connectivities in the network, we could simulate the condition of significant (P < 0.001) decrease in beta band power and a simultaneous increase in the theta band power, similar to that observed in Schizophrenia patients. Overall, we have shown that the connectivity changes in a simple visual thalamocortical in silico model can simulate state changes in the brain corresponding to both health and disease conditions.

Imprecise Predictive Coding Is at the Core of Classical Schizophrenia

Current diagnostic criteria for schizophrenia place emphasis on delusions and hallucinations, whereas the classical descriptions of schizophrenia by Kraepelin and Bleuler emphasized disorganization and impoverishment of mental activity. Despite the availability of antipsychotic medication for treating delusions and hallucinations, many patients continue to experience persisting disability. Improving treatment requires a better understanding of the processes leading to persisting disability. We recently introduced the term classical schizophrenia to describe cases with disorganized and impoverished mental activity, cognitive impairment and predisposition to persisting disability. Recent evidence reveals that a polygenic score indicating risk for schizophrenia predicts severity of the features of classical schizophrenia: disorganization, and to a lesser extent, impoverishment of mental activity and cognitive impairment. Current understanding of brain function attributes a cardinal role to predictive coding: the process of generating models of the world that are successively updated in light of confirmation or contradiction by subsequent sensory information. It has been proposed that abnormalities of these predictive processes account for delusions and hallucinations. Here we examine the evidence provided by electrophysiology and fMRI indicating that imprecise predictive coding is the core pathological process in classical schizophrenia, accounting for disorganization, psychomotor poverty and cognitive impairment. Functional imaging reveals aberrant brain activity at network hubs engaged during encoding of predictions. We discuss the possibility that frequent prediction errors might promote excess release of the neurotransmitter, dopamine, thereby accounting for the occurrence of episodes of florid psychotic symptoms including delusions and hallucinations in classical schizophrenia. While the predictive coding hypotheses partially accounts for the time-course of classical schizophrenia, the overall body of evidence indicates that environmental factors also contribute. We discuss the evidence that chronic inflammation is a mechanism that might link diverse genetic and environmental etiological factors, and contribute to the proposed imprecision of predictive coding.

Relationship Between Replay-Associated Ripples and Hippocampal N-Methyl-D-Aspartate Receptors: Preliminary Evidence From a PET-MEG Study in Schizophrenia

Background and Hypotheses

Hippocampal replay and associated high-frequency ripple oscillations are among the best-characterized phenomena in resting brain activity. Replay/ripples support memory consolidation and relational inference, and are regulated by N-methyl-D-aspartate receptors (NMDARs). Schizophrenia has been associated with both replay/ripple abnormalities and NMDAR hypofunction in both clinical samples and genetic mouse models, although the relationship between these 2 facets of hippocampal function has not been tested in humans.

Study Design

Here, we avail of a unique multimodal human neuroimaging data set to investigate the relationship between the availability of (intrachannel) NMDAR binding sites in hippocampus, and replay-associated ripple power, in 16 participants (7 nonclinical participants and 9 people with a diagnosis of schizophrenia, PScz). Each participant had both a [¹⁸F]GE-179 positron emission tomography (PET) scan (to measure NMDAR availability, V _T ) and a magnetoencephalography (MEG) scan (to measure offline neural replay and associated high-frequency ripple oscillations, using Temporally Delayed Linear Modeling).

Study Results

We show a positive relationship between hippocampal NMDAR availability and replay-associated ripple power. This linkage was evident across control participants (r(5) = .94, P = .002) and PScz (r(7) = .70, P = .04), with no group difference.

Conclusions

Our findings provide preliminary evidence for a relationship between hippocampal NMDAR availability and replay-associated ripple power in humans, and haverelevance for NMDAR hypofunction theories of schizophrenia.

Is There a Glutathione Centered Redox Dysregulation Subtype of Schizophrenia?

Schizophrenia continues to be an illness with poor outcome. Most mechanistic changes occur many years before the first episode of schizophrenia; these are not reversible after the illness onset. A developmental mechanism that is still modifiable in adult life may center on intracortical glutathione (GSH). A large body of pre-clinical data has suggested the possibility of notable GSH-deficit in a subgroup of patients with schizophrenia. Nevertheless, studies of intracortical GSH are not conclusive in this regard. In this review, we highlight the recent ultra-high field magnetic resonance spectroscopic studies linking GSH to critical outcome measures across various stages of schizophrenia. We discuss the methodological steps required to conclusively establish or refute the persistence of GSH-deficit subtype and clarify the role of the central antioxidant system in disrupting the brain structure and connectivity in the early stages of schizophrenia. We propose in-vivo GSH quantification for patient selection in forthcoming antioxidant trials in psychosis. This review offers directions for a promising non-dopaminergic early intervention approach in schizophrenia.