Confidence biases in problem gambling.

Background and aims: Subjective confidence plays an important role in guiding behaviour, especially when objective feedback is unavailable. Systematic misjudgements in confidence can foster maladaptive behaviours and have been linked to various psychiatric disorders. In this study, we adopted a transdiagnostic approach to examine confidence biases in problem gamblers across three levels: local decision confidence, global task performance confidence, and overall self-esteem. The importance of taking a transdiagnostic perspective is increasingly recognised, as it captures the dimensional nature of psychiatric symptoms that often cut across diagnostic boundaries. Accordingly, we investigated if any observed confidence biases could be explained by transdiagnostic symptom dimensions of Anxiety-Depression and Compulsive Behaviour and Intrusive Thought. This approach allows us to gain a more comprehensive understanding of the role of metacognitive processes in problem gambling, beyond the constraints of traditional diagnostic categories. Methods: Thirty-eight problem gamblers and 38 demographically matched control participants engaged in a gamified metacognition task and completed self-report questionnaires assessing transdiagnostic symptom dimensions. Results: Compared to controls, problem gamblers displayed significantly elevated confidence at the local decision and global task levels, independent of their actual task performance. This elevated confidence was observed even after controlling for the heightened symptom levels of Anxiety-Depression and Compulsive Behaviour and Intrusive Thought among the problem gamblers. Discussion: The results reveal a notable disparity in confidence levels between problem gamblers and control participants, not fully accounted for by the symptom dimensions Anxiety-Depression and Compulsive Behaviour and Intrusive Thought. This suggests the contribution of other factors, perhaps linked to gambling-specific cognitive distortions, to the observed confidence biases. Conclusion: The findings highlight the intricate link between metacognitive confidence and psychiatric symptoms in the context of problem gambling. It underscores the need for further research into metacognitive biases, which could enhance therapeutic approaches for individuals with psychiatric conditions.

Gut microbiota composition links to variation in functional domains across psychiatric disorders.

OBJECTIVE: Changes in microbial composition are observed in various psychiatric disorders, but their specificity to certain symptoms or processes remains unclear. This study explores the associations between the gut microbiota composition and the Research Domain Criteria (RDoC) domains of functioning, representing symptom domains, specifically focusing on stress-related and neurodevelopmental disorders in patients with and without psychiatric comorbidity. METHODS: The gut microbiota was analyzed in 369 participants, comprising 272 individuals diagnosed with a mood disorder, anxiety disorder, attention deficit/hyperactivity disorder, autism spectrum disorder, and/or substance use disorder, as well as 97 psychiatrically unaffected individuals. The RDoC domains were estimated using principal component analysis (PCA) with oblique rotation on a range of psychiatric, psychological, and personality measures. Associations between the gut microbiota and the functional domains were assessed using multiple linear regression and permanova, adjusted for age, sex, diet, smoking, medication use and comorbidity status. RESULTS: Four functional domains, aligning with RDoC's negative valence, social processes, cognitive systems, and arousal/regulatory systems domains, were identified. Significant associations were found between these domains and eight microbial genera, including associations of negative valence with the abundance of the genera Sellimonas, CHKCI001, Clostridium sensu stricto 1, Oscillibacter, and Flavonifractor; social processes with Sellimonas; cognitive systems with Sporobacter and Hungatella; and arousal/regulatory systems with Ruminococcus torques (all pFDR < 0.05). CONCLUSION: Our findings demonstrate associations between the gut microbiota and the domains of functioning across patients and unaffected individuals, potentially mediated by immune-related processes. These results open avenues for microbiota-focused personalized interventions, considering psychiatric comorbidity. However, further research is warranted to establish causality and elucidate mechanistic pathways.

The characteristics and clinical outcomes of a pluripotent high-risk group with the potential to develop a diverse range of psychiatric disorders.

BACKGROUND: Recent studies have indicated that clinical high risk for psychosis (CHR-P) is highly specific for psychotic disorders other than pluripotential to various serious mental illnesses. However, not all CHR-P develop psychotic disorder only, and psychosis can occur in non-psychotic disorders as well. Our prospective cohort study aims to investigate the characteristics and clinical outcomes of a pluripotent high-risk group with the potential to develop a diverse range of psychiatric disorders. METHODS: The SPRIM study is a prospective naturalistic cohort program that focuses on the early detection of those at risk of developing serious mental illness, including psychosis (CHR-P), bipolar (CHR-B), and depressive disorder (CHR-D), as well as undifferentiated risk participants (UCHR). Our study has a longitudinal design with a baseline assessment and eight follow-up evaluations at 6, 12, 18, 24, 30, 36, 42, and 48 months to determine whether participants have transitioned to psychosis or mood disorders. RESULTS: The SPRIM sample consisted of 90 CHR participants. The total cumulative incidence rate of transition was 53.3% (95% CI 32.5-77.2). CHR-P, CHR-B, CHR-D, and UCHR had cumulative incidence rates of 13.7% (95% CI 3.4-46.4), 52.4% (95% CI 28.1-81.1), 66.7% (95% CI 24.6-98.6) and 54.3% (95% CI 20.5-93.1), respectively. The cumulative incidence of psychosis, bipolar, and depressive disorder among all participants was 3.3% (95% CI 0.8-11.5), 45.7% (95% CI 24.4-73.6), and 11.2% (95% CI 3.1-36.2), respectively. CONCLUSIONS: Our study suggests that the concept of pluripotent high-risk for a diverse range of psychiatric disorders is an integrative approach to examining transdiagnostic interactions between illnesses with a high transition rate and minimizing stigma.

The Associations of Exposome Score with Various Domains of Psychopathology: A Network Analysis in a Non-Clinical Sample.

BACKGROUND: The intricate correlation between environmental exposures and mental health outcomes is increasingly acknowledged in psychiatric research. This study investigated the relationship between cumulative environmental risk factors, as represented by the exposome score (ES), and various domains of psychopathology within a non-clinical sample using a network analysis. METHODS: We recruited 1100 participants (aged 18-35 years, 51.4% females) via a computer-assisted web interview, assessing psychopathological symptoms using standardized questionnaires. Environmental exposures, including season of birth, obstetric complications, advanced paternal age, childhood trauma, cannabis use, and urban upbringing, were self-reported to calculate the ES. RESULTS: A network analysis revealed significant associations of the ES with psychotic-like experiences (PLEs) (weight = 0.113), manic (weight = 0.072), and attention-deficit/hyperactivity disorder symptoms (weight = 0.062). These connections did not differ significantly with respect to their weights. Depressive symptoms had the highest centrality and predictability. The mean predictability across all nodes included in the network was 0.344. CONCLUSIONS: These findings underscore the transdiagnostic nature of environmental exposures, aligning with previous research indicating broad associations between the ES and various facets of psychopathology. Our results suggest that the ES may not specifically correlate with PLEs but may indicate the risk of a broader psychopathology.

Antecedents of major depressive, bipolar, and psychotic disorders: A systematic review and meta-analysis of prospective studies.

Major depressive, bipolar, or psychotic disorders are preceded by earlier manifestations in behaviours and experiences. We present a synthesis of evidence on associations between person-level antecedents (behaviour, performance, psychopathology) in childhood, adolescence, or early adulthood and later onsets of major depressive disorder, bipolar disorder, or psychotic disorder based on prospective studies published up to September 16, 2022. We screened 11,342 records, identified 460 eligible publications, and extracted 570 risk ratios quantifying the relationships between 52 antecedents and onsets in 198 unique samples with prospective follow-up of 122,766 individuals from a mean age of 12.4 to a mean age of 24.8 for 1522,426 person years of follow-up. We completed meta-analyses of 12 antecedents with adequate data. Psychotic symptoms, depressive symptoms, anxiety, disruptive behaviors, affective lability, and sleep problems were transdiagnostic antecedents associated with onsets of depressive, bipolar, and psychotic disorders. Attention-deficit/hyperactivity and hypomanic symptoms specifically predicted bipolar disorder. While transdiagnostic and diagnosis-specific antecedents inform targeted prevention and help understand pathogenic mechanisms, extensive gaps in evidence indicate potential for improving early risk identification.

Shared and Distinct Neurobiological Bases of Bipolar Disorder and Attention-Deficit/Hyperactivity Disorder in Children and Adolescents: A Comparative Meta-analysis of Structural Abnormalities.

OBJECTIVE: Pediatric bipolar disorder (PBD) and attention-deficit/hyperactivity disorder (ADHD) frequently co-occur and share dysfunctions in affective and cognitive domains. As the neural substrates underlying their overlapping and dissociable symptomatology have not been well delineated, a meta-analysis of whole-brain voxel-based morphometry studies in PBD and ADHD was conducted. METHOD: A systematic literature search was performed in PubMed, Web of Science, and Embase. The seed-based d mapping toolbox was used to identify altered clusters of PBD or ADHD and obtain their conjunctive and comparative abnormalities. Suprathreshold patterns were subjected to large-scale network analysis to identify affected brain networks. RESULTS: The search revealed 10 PBD studies (268 patients) and 32 ADHD studies (1,333 patients). Decreased gray matter volumes in the right insula and anterior cingulate cortex relative to typically developing individuals were conjunctive in PBD and ADHD. Reduced volumes in the right inferior frontal gyrus, left orbitofrontal cortex, and hippocampus were more substantial in PBD, while decreased volumes in the left precentral gyrus, left inferior frontal gyrus, and right superior frontal gyrus were more pronounced in ADHD. Neurodevelopmental effects modulated patterns of the left hippocampus in PBD and those of the left inferior frontal gyrus in ADHD. CONCLUSION: These findings suggest that PBD and ADHD are characterized by both common and distinct patterns of gray matter volume alterations. Their overlapping abnormalities may represent a transdiagnostic problem of attention and emotion regulation shared by PBD and ADHD, whereas the disorder-differentiating substrates may contribute to the relative differences in cognitive and affective features that define the 2 disorders. STUDY PREREGISTRATION INFORMATION: Structural Brain Abnormalities of Attention-Deficit/Hyperactivity Disorder and Bipolar Disorder in Children/Adolescents: An Overlapping Meta-analysis; https://osf.io/trg4m.

Advances in the knowledge and therapeutics of schizophrenia, major depression disorder, and bipolar disorder from human brain organoid research.

Tridimensional cultures of human induced pluripotent cells (iPSCs) experimentally directed to neural differentiation, termed "brain organoids" are now employed as an in vitro assay that recapitulates early developmental stages of nervous tissue differentiation. Technical progress in culture methodology enabled the generation of regionally specialized organoids with structural and neurochemical characters of distinct encephalic regions. The technical process of organoid elaboration is undergoing progressively implementation, but current robustness of the assay has attracted the attention of psychiatric research to substitute/complement animal experimentation for analyzing the pathophysiology of psychiatric disorders. Numerous morphological, structural, molecular and functional insights of psychiatric disorders have been uncovered by comparing brain organoids made with iPSCs obtained from control healthy subjects and psychiatric patients. Brain organoids were also employed for analyzing the response to conventional treatments, to search for new drugs, and to anticipate the therapeutic response of individual patients in a personalized manner. In this review, we gather data obtained by studying cerebral organoids made from iPSCs of patients of the three most frequent serious psychiatric disorders: schizophrenia, major depression disorder, and bipolar disorder. Among the data obtained in these studies, we emphasize: (i) that the origin of these pathologies takes place in the stages of embryonic development; (ii) the existence of shared molecular pathogenic aspects among patients of the three distinct disorders; (iii) the occurrence of molecular differences between patients bearing the same disorder, and (iv) that functional alterations can be activated or aggravated by environmental signals in patients bearing genetic risk for these disorders.

Multivariate patterns of disrupted sleep longitudinally predict affective vulnerability to psychosis in 22q11.2 Deletion Syndrome.

22q11.2 deletion syndrome (22q11DS) contributes dramatically to increased genetic risk for psychopathology, and in particular schizophrenia. Sleep disorders, including obstructive sleep apnea (OSA), are also highly prevalent, making 22q11DS a unique model to explore their impact on psychosis vulnerability. Still, the contribution of sleep disturbances to psychosis vulnerability remains unclear. We characterized the sleep phenotype of 69 individuals with 22q11DS and 38 healthy controls with actigraphy and sleep questionnaires. Psychiatric symptoms were measured concomitantly with the baseline sleep assessment and at longitudinal follow-up, 3.58±0.85 years later. We used a novel multivariate partial-least-square-correlation (PLSC) approach to identify sleep patterns combining objective and subjective variables, which correlated with psychiatric symptoms. We dissected longitudinal pathways linking sleep disturbances to psychosis, using multi-layer-network-analysis. 22q11DS was characterized by a non-restorative sleep pattern, combining increased daytime fatigue despite longer sleep duration. Non-restorative sleep combined with OSA symptoms correlated with both emotional and psychotic symptoms. Moreover, a sleep pattern evocative of OSA predicted longitudinal worsening of positive and negative symptoms, by accentuating the effects of emotional dysregulation. These results suggest that sleep disturbances could significantly increase psychosis risk, along an affective pathway. If confirmed, this suggests that systematic screening of sleep quality could mitigate psychosis vulnerability in 22q11DS.

Information extraction from free text for aiding transdiagnostic psychiatry: constructing NLP pipelines tailored to clinicians' needs.

BACKGROUND: Developing predictive models for precision psychiatry is challenging because of unavailability of the necessary data: extracting useful information from existing electronic health record (EHR) data is not straightforward, and available clinical trial datasets are often not representative for heterogeneous patient groups. The aim of this study was constructing a natural language processing (NLP) pipeline that extracts variables for building predictive models from EHRs. We specifically tailor the pipeline for extracting information on outcomes of psychiatry treatment trajectories, applicable throughout the entire spectrum of mental health disorders ("transdiagnostic"). METHODS: A qualitative study into beliefs of clinical staff on measuring treatment outcomes was conducted to construct a candidate list of variables to extract from the EHR. To investigate if the proposed variables are suitable for measuring treatment effects, resulting themes were compared to transdiagnostic outcome measures currently used in psychiatry research and compared to the HDRS (as a gold standard) through systematic review, resulting in an ideal set of variables. To extract these from EHR data, a semi-rule based NLP pipeline was constructed and tailored to the candidate variables using Prodigy. Classification accuracy and F1-scores were calculated and pipeline output was compared to HDRS scores using clinical notes from patients admitted in 2019 and 2020. RESULTS: Analysis of 34 questionnaires answered by clinical staff resulted in four themes defining treatment outcomes: symptom reduction, general well-being, social functioning and personalization. Systematic review revealed 242 different transdiagnostic outcome measures, with the 36-item Short-Form Survey for quality of life (SF36) being used most consistently, showing substantial overlap with the themes from the qualitative study. Comparing SF36 to HDRS scores in 26 studies revealed moderate to good correlations (0.62-0.79) and good positive predictive values (0.75-0.88). The NLP pipeline developed with notes from 22,170 patients reached an accuracy of 95 to 99 percent (F1 scores: 0.38 - 0.86) on detecting these themes, evaluated on data from 361 patients. CONCLUSIONS: The NLP pipeline developed in this study extracts outcome measures from the EHR that cater specifically to the needs of clinical staff and align with outcome measures used to detect treatment effects in clinical trials.

Extracting Circadian and Sleep Parameters from Longitudinal Data in Schizophrenia for the Design of Pragmatic Light Interventions.

Sleep and circadian rhythm dysfunction is prevalent in schizophrenia, is associated with distress and poorer clinical status, yet remains an under-recognized therapeutic target. The development of new therapies requires the identification of the primary drivers of these abnormalities. Understanding of the regulation of sleep-wake timing is now sufficiently advanced for mathematical model-based analyses to identify the relative contribution of endogenous circadian processes, behavioral or environmental influences on sleep-wake disturbance and guide the development of personalized treatments. Here, we have elucidated factors underlying disturbed sleep-wake timing by applying a predictive mathematical model for the interaction of light and the circadian and homeostatic regulation of sleep to actigraphy, light, and melatonin profiles from 20 schizophrenia patients and 21 age-matched healthy unemployed controls, and designed interventions which restored sleep-circadian function. Compared to controls, those with schizophrenia slept longer, had more variable sleep timing, and received significantly fewer hours of bright light (light > 500 lux), which was associated with greater variance in sleep timing. Combining the model with the objective data revealed that non 24-h sleep could be best explained by reduced light exposure rather than differences in intrinsic circadian period. Modeling implied that late sleep offset and non 24-h sleep timing in schizophrenia can be normalized by changes in environmental light-dark profiles, without imposing major lifestyle changes. Aberrant timing and intensity of light exposure patterns are likely causal factors in sleep timing disturbances in schizophrenia. Implementing our new model-data framework in clinical practice could deliver personalized and acceptable light-dark interventions that normalize sleep-wake timing.

How Local and Global Metacognition Shape Mental Health.

Metacognition is the ability to reflect on our own cognition and mental states. It is a critical aspect of human subjective experience and operates across many hierarchical levels of abstraction-encompassing local confidence in isolated decisions and global self-beliefs about our abilities and skills. Alterations in metacognition are considered foundational to neurologic and psychiatric disorders, but research has mostly focused on local metacognitive computations, missing out on the role of global aspects of metacognition. Here, we first review current behavioral and neural metrics of local metacognition that lay the foundation for this research. We then address the neurocognitive underpinnings of global metacognition uncovered by recent studies. Finally, we outline a theoretical framework in which higher hierarchical levels of metacognition may help identify the role of maladaptive metacognitive evaluation in mental health conditions, particularly when combined with transdiagnostic methods.

Model-Based Planning Deficits in Compulsivity Are Linked to Faulty Neural Representations of Task Structure.

Compulsive individuals have deficits in model-based planning, but the mechanisms that drive this have not been established. We examined two candidates-that compulsivity is linked to (1) an impaired model of the task environment and/or (2) an inability to engage cognitive control when making choices. To test this, 192 participants performed a two-step reinforcement learning task with concurrent EEG recordings, and we related the neural and behavioral data to their scores on a self-reported transdiagnostic dimension of compulsivity. To examine subjects' internal model of the task, we used established behavioral and neural responses to unexpected events [reaction time (RT) slowing, P300 wave, and parietal-occipital alpha band power] measured when an unexpected transition occurred. To assess cognitive control, we probed theta power at the time of initial choice. As expected, model-based planning was linked to greater behavioral (RT) and neural (alpha power, but not P300) sensitivity to rare transitions. Critically, the sensitivities of both RT and alpha to task structure were weaker in those high in compulsivity. This RT-compulsivity effect was tested and replicated in an independent pre-existing dataset (N = 1413). We also found that mid-frontal theta power at the time of choice was reduced in highly compulsive individuals though its relation to model-based planning was less pronounced. These data suggest that model-based planning deficits in compulsive individuals may arise, at least in part, from having an impaired representation of the environment, specifically how actions lead to future states.SIGNIFICANCE STATEMENT Compulsivity is linked to poorer performance on tasks that require model-based planning, but it is unclear what precise mechanisms underlie this deficit. Do compulsive individuals fail to engage cognitive control at the time of choice? Or do they have difficulty in building and maintaining an accurate representation of their environment, the foundation needed to behave in a goal-directed manner? With reaction time and EEG measures in 192 individuals who performed a two-step decision-making task, we found that compulsive individuals are less sensitive to surprising action-state transitions, where they slow down less and show less alpha band suppression following a rare transition. These findings implicate failures in maintaining an accurate model of the world in model-based planning deficits in compulsivity.

Perseverative Cognition in the Positive Valence Systems: An Experimental and Ecological Investigation.

Perseverative cognition (PC) is a transdiagnostic risk factor that characterizes both hypo-motivational (e.g., depression) and hyper-motivational (e.g., addiction) disorders; however, it has been almost exclusively studied within the context of the negative valence systems. The present study aimed to fill this gap by combining laboratory-based, computational and ecological assessments. Healthy individuals performed the Probabilistic Reward Task (PRT) before and after the induction of PC or a waiting period. Computational modeling was applied to dissociate the effects of PC on reward sensitivity and learning rate. Afterwards, participants underwent a one-week ecological momentary assessment of daily PC occurrence, as well as anticipatory and consummatory reward-related behavior. Induction of PC led to increased response bias on the PRT compared to waiting, likely due to an increase in learning rate but not in reward sensitivity, as suggested by computational modeling. In daily-life, PC increased the discrepancy between expected and obtained rewards (i.e., prediction error). Current converging experimental and ecological evidence suggests that PC is associated with abnormalities in the functionality of positive valence systems. Given the role of PC in the prediction, maintenance, and recurrence of psychopathology, it would be clinically valuable to extend research on this topic beyond the negative valence systems.

Resting-state brain oscillations predict cognitive function in psychiatric disorders: A transdiagnostic machine learning approach.

BACKGROUND: Cognitive dysfunction is widespread in psychiatric disorders and can significantly impact quality of life. Deficits cut across traditional diagnostic boundaries, necessitating new approaches to understand how cognitive function relates to large-scale brain activity and psychiatric symptoms across the diagnostic spectrum. OBJECTIVE: Using random forest regression, we aimed to identify transdiagnostic patterns linking cognitive function to resting-state EEG oscillations. METHODS: 216 participants recruited through an outpatient psychiatric clinic completed the Cambridge Neuropsychological Test Automated Battery and underwent a 5-minute eyes-closed resting state EEG recording. We built random forest regression models to predict performance on each cognitive test using the resting-state EEG power spectrum as input, and we compared model performance to a sampling distribution constructed with random permutations. For models that performed significantly better than chance, we used feature importance estimates to identify features of the EEG power spectrum that are predictive of cognitive functioning. RESULTS: Random forest models successfully predicted performance on measures of episodic memory and associative learning (Paired Associates Learning, PAL), information processing speed (Choice Reaction Time, CRT), and attentional set-shifting and executive function (Intra-Extra Dimensional Set Shift, IED). Oscillatory power in the upper alpha range was associated with better performance on PAL and CRT, while low alpha power was associated with worse CRT performance. Beta power predicted poor performance on all three tests. Theta power was associated with good performance on PAL, and delta and theta oscillations were identified as predictors of good performance on IED. No differences in cognitive performance were found between diagnostic categories. CONCLUSION: Resting oscillations are predictive of certain dimensions of cognitive function across various psychiatric disorders. These findings may inform treatment development to improve cognition.

The impact of transcranial Direct Current stimulation on rumination: A systematic review of the sham-controlled studies in healthy and clinical samples.

INTRODUCTION: Broadly considered a transdiagnostic feature of psychological disorders, rumination is associated with lower treatment response, slower recovery rates, and higher relapse rates. Accordingly, research has focused on the development of interventions to alleviate rumination. Recently, transcranial Direct Current Stimulation (tDCS) has emerged as a promising tool to do so. METHODS: We performed a systematic review of sham-controlled tDCS studies targeting rumination among healthy participants or patients with psychiatric disorders, investigating the effectiveness of tDCS in reducing rumination, and assessing the research quality of this nascent field. RESULTS: We identified nine studies, with five reporting a significant impact of tDCS on rumination. We also outlined a few tDCS parameters (e.g., stimulation duration, electrode size) and research methods' features (e.g., within- versus between-research designs) characterizing those positive-finding studies. However, these studies were characterized by substantial heterogeneity (e.g., methodological flaws, lack of open science practices), precluding any definite statement about the best way to target rumination via tDCS. Moreover, several strong methodological limitations were also present across those studies. DISCUSSION: Although our systematic review identifies the strengths and weaknesses of the available research about the impact of tDCS on rumination, it calls for strong efforts to improve this nascent field's current methodological caveats. We discuss how open science practices can help to usher this field forward.

A dimensional investigation of error-related negativity (ERN) and self-reported psychiatric symptoms.

Alterations in error processing are implicated in a range of DSM-defined psychiatric disorders. For instance, obsessive-compulsive disorder (OCD) and generalised anxiety disorder show enhanced electrophysiological responses to errors-i.e. error-related negativity (ERN)-while others like schizophrenia have an attenuated ERN. However, as diagnostic categories in psychiatry are heterogeneous and also highly intercorrelated, the precise mapping of ERN enhancements/impairments is unclear. To address this, we recorded electroencephalograms (EEG) from 196 participants who performed the Flanker task and collected scores on 9 questionnaires assessing psychiatric symptoms to test if a dimensional framework could reveal specific transdiagnostic clinical manifestations of error processing dysfunctions. Contrary to our hypothesis, we found non-significant associations between ERN amplitude and symptom severity of OCD, trait anxiety, depression, social anxiety, impulsivity, eating disorders, alcohol addiction, schizotypy and apathy. A transdiagnostic approach did nothing to improve signal; there were non-significant associations between all three transdiagnostic dimensions (anxious-depression, compulsive behaviour and intrusive thought, and social withdrawal) and ERN magnitude. In these same individuals, we replicated a previously published transdiagnostic association between goal-directed learning and compulsive behaviour and intrusive thought. Possible explanations discussed are (i) that associations between the ERN and psychopathology might be smaller than previously assumed, (ii) that these associations might depend on a greater level of symptom severity than other transdiagnostic cognitive biomarkers, or (iii) that task parameters, such as the ratio of compatible to incompatible trials, might be crucial for ensuring the sensitivity of the ERN to clinical phenomena.

Beyond the "at risk mental state" concept: transitioning to transdiagnostic psychiatry.

The "at risk mental state" for psychosis approach has been a catalytic, highly productive research paradigm over the last 25 years. In this paper we review that paradigm and summarize its key lessons, which include the valence of this phenotype for future psychosis outcomes, but also for comorbid, persistent or incident non-psychotic disorders; and the evidence that onset of psychotic disorder can at least be delayed in ultra high risk (UHR) patients, and that some full-threshold psychotic disorder may emerge from risk states not captured by UHR criteria. The paradigm has also illuminated risk factors and mechanisms involved in psychosis onset. However, findings from this and related paradigms indicate the need to develop new identification and diagnostic strategies. These findings include the high prevalence and impact of mental disorders in young people, the limitations of current diagnostic systems and risk identification approaches, the diffuse and unstable symptom patterns in early stages, and their pluripotent, transdiagnostic trajectories. The approach we have recently adopted has been guided by the clinical staging model and adapts the original "at risk mental state" approach to encompass a broader range of inputs and output target syndromes. This approach is supported by a number of novel modelling and prediction strategies that acknowledge and reflect the dynamic nature of psychopathology, such as dynamical systems theory, network theory, and joint modelling. Importantly, a broader transdiagnostic approach and enhancing specific prediction (profiling or increasing precision) can be achieved concurrently. A holistic strategy can be developed that applies these new prediction approaches, as well as machine learning and iterative probabilistic multimodal models, to a blend of subjective psychological data, physical disturbances (e.g., EEG measures) and biomarkers (e.g., neuroinflammation, neural network abnormalities) acquired through fine-grained sequential or longitudinal assessments. This strategy could ultimately enhance our understanding and ability to predict the onset, early course and evolution of mental ill health, further opening pathways for preventive interventions.