Validating dimensions of psychosis symptomatology: Neural correlates and 20-year outcomes

Emotional intelligence as a predictor of functional outcomes in psychotic disorders

Psychotic disorders are associated with significant impairment in psychosocial functioning, yet mechanisms associated with this impairment remain poorly understood. Emotional intelligence, a component of social cognition, is associated with psychosocial functioning in this population. However, prior work has used relatively small samples, reported inconsistent relations between functioning domains and emotional intelligence, and inconsistently considered negative symptoms. To address these limitations, we examined the predictive ability of emotional intelligence on functional outcomes using a five-year longitudinal design. We used a large sample of individuals with and without psychotic disorder diagnoses (N = 324), a performance-based measure of emotional intelligence, and three measures of functioning (i.e., social performance, assessor-rated social and occupational functioning, self-rated functioning in independent living). Results revealed individuals diagnosed with a psychotic disorder have lower emotional intelligence than those without a history of psychosis. Emotional intelligence was associated with social performance and social and occupational functioning in both those with and without a history of psychosis. In those diagnosed with a psychotic disorder, emotional intelligence and negative symptoms better predict social performance (βEmotional = 0.36, R2delta = 0.09) and social and occupational functioning (βEmotional = 0.21, R2 = 0.03), but not self-rated functioning in independent living (βEmotional = -0.08, R2delta = 0.00), as compared to negative symptoms alone. Overall, findings support the use of emotional intelligence as a longitudinal predictor of social and occupational outcomes above and beyond negative symptoms alone. This work highlights potential, specific intervention targets for individuals with psychotic disorders.

Impact of ERP Reliability Cutoffs on Sample Characteristics and Effect Sizes: Performance-Monitoring ERPs in Psychosis and Healthy Controls

In studies of event-related brain potentials (ERPs), it is common practice to exclude participants for having too few trials for analysis to ensure adequate score reliability (i.e., internal consistency). However, in research involving clinical samples, the impact of increasingly rigorous reliability standards on factors such as sample generalizability, patient versus control effect sizes, and effect sizes for within-group correlations with external variables is unclear. This study systematically evaluated whether different ERP reliability cutoffs impacted these factors in psychosis. Error-related negativity (ERN) and error positivity (Pe) were assessed during a modified flanker task in 97 patients with psychosis and 104 healthy comparison participants, who also completed measures of cognition and psychiatric symptoms. ERP reliability cutoffs had notably different effects on the factors considered. A recommended reliability cutoff of 0.80 resulted in sample bias due to systematic exclusion of patients with relatively few task errors, lower reported psychiatric symptoms, and higher levels of cognitive functioning. ERP score reliability lower than 0.80 resulted in generally smaller between- and within-group effect sizes, likely misrepresenting effect sizes. Imposing rigorous ERP reliability standards in studies of psychotic disorders might exclude high-functioning patients, which raises important considerations for the generalizability of clinical ERP research. Moving forward, we recommend examining characteristics of excluded participants, optimizing paradigms and processing pipelines for use in clinical samples, justifying reliability thresholds, and routinely reporting score reliability of all measurements, ERP or otherwise, used to examine individual differences, especially in clinical research.

The interaction network and potential clinical effectiveness of dimensional psychopathology phenotyping based on EMR: a Bayesian network approach

The current DSM-oriented diagnostic paradigm has introduced the issue of heterogeneity, as it fails to account for the identification of the neurological processes underlying mental illnesses, which affects the precision of treatment. The Research Domain Criteria (RDoC) framework serves as a recognized approach to addressing this heterogeneity, and several assessment and translation techniques have been proposed. Among these methods, transforming RDoC scores from electronic medical records (EMR) using Natural Language Processing (NLP) has emerged as a suitable technique, demonstrating clinical effectiveness. Numerous studies have sought to use RDoC to understand the Diagnostic and Statistical Manual of Mental Disorders (DSM) categories from a qualified perspective, but few studies have examined the distribution variations and interaction characteristics of RDoC within various DSM categories through retrospective analyses. Therefore, we employed unsupervised learning to translate five domains of eRDoC scores derived from electronic medical records (EMR) of patients diagnosed with Major Depressive Disorder (MDD), Schizophrenia (SCZ), and Bipolar Disorder (BD) at West China Hospital between 2008 and 2021. The distribution characteristics, interaction networks, and potential clinical effectiveness of RDoC domains were analyzed. Using non-parametric statistical tests, we found that MDD had the highest score in Negative Valence System (NVS) (4.1, p < 0.001), while BD exhibited the highest score in Positive Valence System (PVS) score (4.9, p < 0.001) and Arousal System (AS) (4.4, p < 0.001). SCZ demonstrated the highest scores in Cognitive Systems (CS) (5.8, p < 0.001) and Social Processes Systems (SPS) (4.6, p < 0.001). Through Bayesian network (BN) analysis, we identified relatively consistent interaction relationships among various RDoC domains (NVS → AS, NVS → CS, NVS → PVS, as well as CS → SPS; parameter range = 0.156 to 0.635, p < 0.001). Lastly, using logistic regression and Cox proportional hazards models, we demonstrated that AS was significantly associated with the length of hospital stay (-0.21, p < 0.05) and 30-day readmission risk (adjusted odds ratio [aOR] = 0.91, 95% confidence interval [CI] 0.91-0.99) to some extent. In conclusion, we suggest that the eRDoC characteristics varied in different DSM. By Bayesian Network, we found NVS and CS might be potential source in interacting with other system. Furthermore, CS, SPS and AS were associated with the length of stay and 30-days readmission, making them effective for predicting prognosis of psychiatric disorders.

DSM-5 self-rated level 1 cross-cutting symptom measure (CCSM1): Proposal for a three-factors model and implications for the assessment of at-risk situations

OBJECTIVE

The aim of this paper is to study the measurement of the DSM5 self-rated transversal symptoms level 1 (CCSM1) from a dimensional perspective in line with current models of psychopathology in three factors: internalization, thought disorders, externalization.

METHOD

Based on the 670 non-clinical protocols we collected, we verified that the VSS is composed of three factors. We studied the 3-factor composition with half of the sample and confirmed this composition with the other half. To show that these three factors were more relevant than the original 13 dimensions, we compared the results to three clinical groups and, after a cluster analysis, we investigated the intensity and frequency of people at risk across the original dimensions.

RESULTS

While the 13 initial dimensions of the CCSM1 do not completely differentiate this sample from the clinical groups, the three high-order dimensions are discriminating. Clustering confirms these results when comparing the least and most affected subjects and allows us to see that these three HODs have significant impacts on the observation of cases at risk of clinical disorders in this non-clinical sample.

DISCUSSION

To be further validated, these three HODs should be studied in relation to tools that assess internalization, thought disorders or externalization.

Prediction of Clinical Outcomes in Psychotic Disorders Using Artificial Intelligence Methods: A Scoping Review

BACKGROUND

Psychotic disorders are major psychiatric disorders that can impact multiple domains including physical, social, and psychological functioning within individuals with these conditions. Being able to better predict the outcomes of psychotic disorders will allow clinicians to identify illness subgroups and optimize treatment strategies in a timely manner.

OBJECTIVE

In this scoping review, we aimed to examine the accuracy of the use of artificial intelligence (AI) methods in predicting the clinical outcomes of patients with psychotic disorders as well as determine the relevant predictors of these outcomes.

METHODS

This review was guided by the PRISMA Guidelines for Scoping Reviews. Seven electronic databases were searched for relevant published articles in English until 1 February 2024.

RESULTS

Thirty articles were included in this review. These studies were mainly conducted in the West (63%) and Asia (37%) and published within the last 5 years (83.3%). The clinical outcomes included symptomatic improvements, illness course, and social functioning. The machine learning models utilized data from various sources including clinical, cognitive, and biological variables such as genetic, neuroimaging measures. In terms of main machine learning models used, the most common approaches were support vector machine, random forest, logistic regression, and linear regression models. No specific machine learning approach outperformed the other approaches consistently across the studies, and an overall range of predictive accuracy was observed with an AUC from 0.58 to 0.95. Specific predictors of clinical outcomes included demographic characteristics (gender, socioeconomic status, accommodation, education, and employment); social factors (activity level and interpersonal relationships); illness features (number of relapses, duration of relapses, hospitalization rates, cognitive impairments, and negative and disorganization symptoms); treatment (prescription of first-generation antipsychotics, high antipsychotic doses, clozapine, use of electroconvulsive therapy, and presence of metabolic syndrome); and structural and functional neuroimaging abnormalities, especially involving the temporal and frontal brain regions.

CONCLUSIONS

The current review highlights the potential and need to further refine AI and machine learning models in parsing out the complex interplay of specific variables that contribute to the clinical outcome prediction of psychotic disorders.

Behavioral meaures of psychotic disorders: Using automatic facial coding to detect nonverbal expressions in video

Emotional deficits in psychosis are prevalent and difficult to treat. In particular, much remains unknown about facial expression abnormalities, and a key reason is that expressions are very labor-intensive to code. Automatic facial coding (AFC) can remove this barrier. The current study sought to both provide evidence for the utility of AFC in psychosis for research purposes and to provide evidence that AFC are valid measures of clinical constructs. Changes of facial expressions and head position of participants-39 with schizophrenia/schizoaffective disorder (SZ), 46 with other psychotic disorders (OP), and 108 never psychotic individuals (NP)-were assessed via FaceReader, a commercially available automated facial expression analysis software, using video recorded during a clinical interview. We first examined the behavioral measures of the psychotic disorder groups and tested if they can discriminate between the groups. Next, we evaluated links of behavioral measures with clinical symptoms, controlling for group membership. We found the SZ group was characterized by significantly less variation in neutral expressions, happy expressions, arousal, and head movements compared to NP. These measures discriminated SZ from NP well (AUC = 0.79, sensitivity = 0.79, specificity = 0.67) but discriminated SZ from OP less well (AUC = 0.66, sensitivity = 0.77, specificity = 0.46). We also found significant correlations between clinician-rated symptoms and most behavioral measures (particularly happy expressions, arousal, and head movements). Taken together, these results suggest that AFC can provide useful behavioral measures of psychosis, which could improve research on non-verbal expressions in psychosis and, ultimately, enhance treatment.

Long-Term Course of Remission and Recovery in Psychotic Disorders

OBJECTIVE

Understanding prognosis is critical to anticipating public health needs and providing care to individuals with psychotic disorders. However, the long-term course of remission and recovery remains unclear. In this study, the most common trajectories of illness course are described for a cohort of individuals followed for 25 years since first admission for psychosis.

METHODS

Participants are from the Suffolk County Mental Health Project, an epidemiological study of first-admission psychosis. Data for the present study was collected from six follow-ups, with 311 individuals assessed at the 25-year follow-up. Common patterns of remission and recovery were assessed in the baseline cohort of 591 individuals and the subsample from the 25-year follow up.

RESULTS

In the baseline cohort and the 25-year subsample, the most common trajectory for individuals with schizophrenia spectrum disorders was no remission and no recovery. Among individuals with other psychotic disorders, in both the baseline and 25-year cohorts, the modal pattern was one of intermittent remission and recovery. Individuals with other psychotic disorders were more likely to experience stable remission (15.1%) and stable recovery (21.1%), outcomes that were rare among individuals with schizophrenia spectrum disorders (0% and 0.6%, respectively).

CONCLUSIONS

The modal longitudinal pattern for individuals with other psychoses is one of multiple transitions into and out of symptomatic and functional recovery. Engagement in a long-term health care plan may help individuals detect and respond to these changes. Sustained remission and recovery are rare among people with schizophrenia spectrum disorders. Efforts should be directed toward developing more effective treatments for this population.

Psychosis superspectrum II: neurobiology, treatment, and implications

Alternatives to traditional categorical diagnoses have been proposed to improve the validity and utility of psychiatric nosology. This paper continues the companion review of an alternative model, the psychosis superspectrum of the Hierarchical Taxonomy of Psychopathology (HiTOP). The superspectrum model aims to describe psychosis-related psychopathology according to data on distributions and associations among signs and symptoms. The superspectrum includes psychoticism and detachment spectra as well as narrow subdimensions within them. Auxiliary domains of cognitive deficit and functional impairment complete the psychopathology profile. The current paper reviews evidence on this model from neurobiology, treatment response, clinical utility, and measure development. Neurobiology research suggests that psychopathology included in the superspectrum shows similar patterns of neural alterations. Treatment response often mirrors the hierarchy of the superspectrum with some treatments being efficacious for psychoticism, others for detachment, and others for a specific subdimension. Compared to traditional diagnostic systems, the quantitative nosology shows an approximately 2-fold increase in reliability, explanatory power, and prognostic accuracy. Clinicians consistently report that the quantitative nosology has more utility than traditional diagnoses, but studies of patients with frank psychosis are currently lacking. Validated measures are available to implement the superspectrum model in practice. The dimensional conceptualization of psychosis-related psychopathology has implications for research, clinical practice, and public health programs. For example, it encourages use of the cohort study design (rather than case-control), transdiagnostic treatment strategies, and selective prevention based on subclinical symptoms. These approaches are already used in the field, and the superspectrum provides further impetus and guidance for their implementation. Existing knowledge on this model is substantial, but significant gaps remain. We identify outstanding questions and propose testable hypotheses to guide further research. Overall, we predict that the more informative, reliable, and valid characterization of psychopathology offered by the superspectrum model will facilitate progress in research and clinical care.

Automating the analysis of facial emotion expression dynamics: A computational framework and application in psychotic disorders

Facial emotion expressions play a central role in interpersonal interactions; these displays are used to predict and influence the behavior of others. Despite their importance, quantifying and analyzing the dynamics of brief facial emotion expressions remains an understudied methodological challenge. Here, we present a method that leverages machine learning and network modeling to assess the dynamics of facial expressions. Using video recordings of clinical interviews, we demonstrate the utility of this approach in a sample of 96 people diagnosed with psychotic disorders and 116 never-psychotic adults. Participants diagnosed with schizophrenia tended to move from neutral expressions to uncommon expressions (e.g., fear, surprise), whereas participants diagnosed with other psychoses (e.g., mood disorders with psychosis) moved toward expressions of sadness. This method has broad applications to the study of normal and altered expressions of emotion and can be integrated with telemedicine to improve psychiatric assessment and treatment.

Psychosis superspectrum I: Nosology, etiology, and lifespan development

This review describes the Hierarchical Taxonomy of Psychopathology (HiTOP) model of psychosis-related psychopathology, the psychosis superspectrum. The HiTOP psychosis superspectrum was developed to address shortcomings of traditional diagnoses for psychotic disorders and related conditions including low reliability, arbitrary boundaries between psychopathology and normality, high symptom co-occurrence, and heterogeneity within diagnostic categories. The psychosis superspectrum is a transdiagnostic dimensional model comprising two spectra-psychoticism and detachment-which are in turn broken down into fourteen narrow components, and two auxiliary domains-cognition and functional impairment. The structure of the spectra and their components are shown to parallel the genetic structure of psychosis and related traits. Psychoticism and detachment have distinct patterns of association with urbanicity, migrant and ethnic minority status, childhood adversity, and cannabis use. The superspectrum also provides a useful model for describing the emergence and course of psychosis, as components of the superspectrum are relatively stable over time. Changes in psychoticism predict the onset of psychosis-related psychopathology, whereas changes in detachment and cognition define later course. Implications of the superspectrum for genetic, socio-environmental, and longitudinal research are discussed. A companion review focuses on neurobiology, treatment response, and clinical utility of the superspectrum, and future research directions.

Translating the hierarchical taxonomy of psychopathology (HiTOP) from potential to practice: Ten research questions

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a novel diagnostic system grounded in empirical research into the architecture of mental illness. Its basic units are continuous dimensions-as opposed to categories-that are organized into a hierarchy according to patterns of symptom co-occurrence observed in quantitative studies. Previous HiTOP discussions have focused on existing evidence regarding the model's structure and ability to account for neurobiological, social, cultural, and clinical variation. The present article looks ahead to the next decade of applied research and clinical practice using the HiTOP rubric. We highlight 10 topics where HiTOP has the potential to make significant breakthroughs. Research areas include genetic influences, environmental contributions, neural mechanisms, real-time dynamics, and lifespan development of psychopathology. We also discuss development of novel assessments, forecasting methods, and treatments. Finally, we consider implications for clinicians and educators. For each of these domains, we propose directions for future research and venture hypotheses as to what HiTOP will reveal about psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Validation of hierarchical taxonomy in a clinical sample

BACKGROUND

Quantitatively derived dimensional models of psychopathology enjoy overwhelming empirical support, and a large and active community of psychopathology researchers has been establishing an empirically based dimensional hierarchical taxonomy of psychopathology (or HiTOP) as a strong candidate replacement for the current categorical classification system. The hierarchical nature of this taxonomy implies that different levels of resolution are likely to be optimal for different purposes. Our aim was to identify which level of detail is likely to provide optimal validity and explanatory power with regard to relevant clinical variables.

METHODS

In the present report from the Rhode Island Methods to Improve Diagnostic Assessment and Services project, we used data from a sample of 2900 psychiatric outpatients to compare different levels from a bass-ackwards model of psychopathology in relation to psychosocial impairment across different domains (global functioning, inability to work, social functioning, suicidal ideation, history of suicide attempts, history of psychiatric hospitalization).

RESULTS

All functioning indices were significantly associated with general psychopathology, but more complex levels provided significant incremental validity. The optimal level of complexity varied across functioning indices, suggesting that there is no single 'best' level for understanding relations between psychopathology and functioning.

CONCLUSIONS

Results support the hierarchical organization of psychopathology dimensions with regard to validity considerations and downstream implications for applied assessment. It would be fruitful to develop and implement measurement of these dimensions at the appropriate level for the purpose at hand. These findings can be used to guide HiTOP-consistent assessment in other research and clinical settings.

Dissecting Schizotypy and Its Association With Cognition and Polygenic Risk for Schizophrenia in a Nonclinical Sample

Schizotypy is a multidimensional construct that captures a continuum of risk for developing schizophrenia-spectrum psychopathology. Existing 3-factor models of schizotypy, consisting of positive, negative, and disorganized dimensions have yielded mixed evidence of genetic continuity with schizophrenia using polygenic risk scores. Here, we propose an approach that involves splitting positive and negative schizotypy into more specific subdimensions that are phenotypically continuous with distinct positive symptoms and negative symptoms recognized in clinical schizophrenia. We used item response theory to derive high-precision estimates of psychometric schizotypy using 251 self-report items obtained from a non-clinical sample of 727 (424 females) adults. These subdimensions were organized hierarchically using structural equation modeling into 3 empirically independent higher-order dimensions enabling associations with polygenic risk for schizophrenia to be examined at different levels of phenotypic generality and specificity. Results revealed that polygenic risk for schizophrenia was associated with variance specific to delusional experiences (γ = 0.093, P = .001) and reduced social interest and engagement (γ = 0.076, P = .020), and these effects were not mediated via the higher-order general, positive, or negative schizotypy factors. We further fractionated general intellectual functioning into fluid and crystallized intelligence in 446 (246 females) participants that underwent onsite cognitive assessment. Polygenic risk scores explained 3.6% of the variance in crystallized intelligence. Our precision phenotyping approach could be used to enhance the etiologic signal in future genetic association studies and improve the detection and prevention of schizophrenia-spectrum psychopathology.

An overview of clustering methods with guidelines for application in mental health research

Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements. In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently introduced. How to choose algorithms to address common issues as well as methods for pre-clustering data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms, we provide information on R functions and libraries.

The validity of transdiagnostic factors in predicting homotypic and heterotypic continuity of psychopathology symptoms over time

Studies of the continuity of psychopathology symptoms mainly involved the traditional conceptualization that mental disorders are discrete entities. However, high comorbidity rates suggest a few transdiagnostic factors that underlie individual disorders. Therefore, the present study examined the validity of transdiagnostic factors in predicting homotypic and heterotypic continuity of comorbidity classes across two waves in a nationally representative sample. We conducted a latent transition analysis to investigate how transdiagnostic factors differentially affect the transition probabilities of comorbidity classes across time. Results found a notable predictive validity of transdiagnostic factors: (a) internalizing strongly predicted the stability of the internalizing class and transition from the externalizing class to internalizing class, and (b) externalizing predicted the transition from the internalizing class to externalizing class. The study also found a more dynamic prediction pattern leading to equifinality and multifinality of psychopathology symptoms. The findings suggest that transdiagnostic factors can provide information on how individuals' symptom manifestations change over time, highlighting the potential benefits of incorporating transdiagnostic factors into assessment, treatment, and prevention.

Using dimensionality-reduction techniques to understand the organization of psychotic symptoms in persistent psychotic illness and first episode psychosis

Psychotic disorders are highly heterogeneous. Understanding relationships between symptoms will be relevant to their underlying pathophysiology. We apply dimensionality-reduction methods across two unique samples to characterize the patterns of symptom organization. We analyzed publicly-available data from 153 participants diagnosed with schizophrenia or schizoaffective disorder (fBIRN Data Repository and the Consortium for Neuropsychiatric Phenomics), as well as 636 first-episode psychosis (FEP) participants from the Prevention and Early Intervention Program for Psychosis (PEPP-Montreal). In all participants, the Scale for the Assessment of Positive Symptoms (SAPS) and Scale for the Assessment of Negative Symptoms (SANS) were collected. Multidimensional scaling (MDS) combined with cluster analysis was applied to SAPS and SANS scores across these two groups of participants. MDS revealed relationships between items of SAPS and SANS. Our application of cluster analysis to these results identified: 1 cluster of disorganization symptoms, 2 clusters of hallucinations/delusions, and 2 SANS clusters (asocial and apathy, speech and affect). Those reality distortion items which were furthest from auditory hallucinations had very weak to no relationship with hallucination severity. Despite being at an earlier stage of illness, symptoms in FEP presentations were similarly organized. While hallucinations and delusions commonly co-occur, we found that their specific themes and content sometimes travel together and sometimes do not. This has important implications, not only for treatment, but also for research-particularly efforts to understand the neurocomputational and pathophysiological mechanism underlying delusions and hallucinations.

Psychopathological networks in psychosis: Changes over time and clinical relevance. A long-term cohort study of first-episode psychosis

BACKGROUND

First-episode psychosis is a critical period for early interventions to reduce the risk of poor outcomes and relapse as much as possible. However, uncertainties about the long-term outcomes of symptomatology remain to be ascertained.

METHODS

The aim of the present study was to use network analysis to investigate first-episode and long-term stages of psychosis at three levels of analysis: micro, meso and macro. The sample was a cohort of 510 patients with first-episode psychoses from the SEGPEP study, who were reassessed at the long-term follow-up (n = 243). We used the Comprehensive Assessment of Symptoms and History for their assessments and lifetime outcome variables of clinical relevance.

RESULTS

Our results showed a similar pattern of clustering between first episodes and long-term follow-up in seven psychopathological dimensions at the micro level, 3 and 4 dimensions at the meso level, and one at the macro level. They also revealed significant differences between first-episode and long-term network structure and centrality measures at the three levels, showing that disorganization symptoms have more influence in long-term stabilized patients.

CONCLUSIONS

Our findings suggest a relative clustering invariance at all levels, with the presence of two domains of disorganization as the most notorious difference over time at micro level. The severity of disorganization at the follow-up was associated with a more severe course of the psychosis. Moreover, a relative stability in global strength of the interconnections was found, even though the network structure varied significantly in the long-term follow-up. The macro level was helpful in the integration of all dimensions into a common psychopathology factor, and in unveiling the strong relationships of psychopathological dimensions with lifetime outcomes, such as negative with poor functioning, disorganization with high antipsychotic dose-years, and delusions with poor adherence to treatment. These results add evidence to the hierarchical, dimensional and longitudinal structure of psychopathological symptoms and their clinical relevance in first-episode psychoses.

Are the epigenetic changes predictive of therapeutic efficacy for psychiatric disorders? A translational approach towards novel drug targets

The etiopathogenesis of mental disorders is not fully understood and accumulating evidence support that clinical symptomatology cannot be assigned to a single gene mutation, but it involves several genetic factors. More specifically, a tight association between genes and environmental risk factors, which could be mediated by epigenetic mechanisms, may play a role in the development of mental disorders. Several data suggest that epigenetic modifications such as DNA methylation, post-translational histone modification and interference of microRNA (miRNA) or long non-coding RNA (lncRNA) may modify the severity of the disease and the outcome of the therapy. Indeed, the study of these mechanisms may help to identify patients particularly vulnerable to mental disorders and may have potential utility as biomarkers to facilitate diagnosis and treatment of psychiatric disorders. This article summarizes the most relevant preclinical and human data showing how epigenetic modifications can be central to the therapeutic efficacy of antidepressant and/or antipsychotic agents, as possible predictor of drugs response.

Pleasant and unpleasant odor identification ability is associated with distinct dimensions of negative symptoms transdiagnostically in psychotic disorders

Negative symptoms are among the greatest sources of functional impairment for individuals with schizophrenia, yet their mechanisms remain poorly understood. Olfactory impairment is associated with negative symptoms. The processing of pleasant olfactory stimuli is subserved by reward-related neural circuitry while unpleasant olfactory processing is subserved by emotion-related neural circuitry, suggesting that these two odor dimensions may offer a window into differential mechanisms of negative symptoms. We examined whether pleasant and unpleasant odor identification bears differential relationships with avolition and inexpressivity dimensions of negative symptoms, whether these relationships are transdiagnostic, and whether pleasant and unpleasant odor processing also relate differently to other domains of functioning in a sample of individuals diagnosed with schizophrenia (N = 54), other psychotic disorders (N = 65), and never-psychotic adults (N = 160). Hierarchical regressions showed that pleasant odor identification was uniquely associated with avolition, while unpleasant odor identification was uniquely associated with inexpressivity. These relationships were largely transdiagnostic across groups. Additionally, pleasant and unpleasant odor identification displayed signs of specificity with other functional and cognitive measures. These results align with past work suggesting dissociable pathomechanisms of negative symptoms and provide a potential avenue for future work using valence-specific olfactory dysfunction as a semi-objective and low-cost marker for understanding and predicting the severity of specific negative symptom profiles.

Load-dependent functional connectivity deficits during visual working memory in first-episode psychosis

INTRODUCTION

Aberrant network connectivity is a core deficit in schizophrenia and may underlie many of its associated cognitive deficits. Previous work in first-episode schizophrenia spectrum illness (FESz) suggests preservation of working memory network function during low-load conditions with dysfunction emerging as task complexity increases. This study assessed visual network connectivity and its contribution to load-dependent working memory impairments.

METHODS

Magnetoencephalography was recorded from 35 FESz and 28 matched controls (HC) during a lateralized change detection task. Impaired alpha desynchronization was previously identified within bilateral dorsal occipital (Occ) regions. Here, whole-brain alpha-band connectivity was examined using phase-locking (PLV) and bilateral Occ as connectivity seeds. Load effects on connectivity were assessed across participants, and PLV modulation within networks was compared between groups.

RESULTS

Occ exhibited significant load modulated connectivity with six regions (FDR-corrected). HC exhibited PLV enhancement with load in all connections. FESz failed to show PLV modulation between right Occ and left inferior frontal gyrus, lateral occipito-temporal sulcus, and anterior intermediate parietal sulcus. Smaller PLVs in all three network connections during both memory load conditions were associated with increased reality distortion in FESz (FDR-corrected.) CONCLUSION: Examination of functional connectivity across the visual working memory network in FESz revealed an inability to enhance communication between perceptual and executive networks in response to increasing cognitive demands. Furthermore, the degree of network communication impairment was associated with positive symptoms. These findings provide insights into the nature of brain dysconnectivity and its contribution to symptoms in early psychosis and identify potential targets for future interventions.

The Course of General Cognitive Ability in Individuals With Psychotic Disorders

Importance

Schizophrenia is associated with major cognitive deficits and has been conceptualized as both a neurodevelopmental and a neurodegenerative disorder. However, when deficits develop and how they change over the course of illness is uncertain.

Objective

To trace cognition from elementary school to old age to test neurodevelopmental and neurodegenerative theories of psychotic disorders.

Design, Setting, and Participants

Data were taken from the Suffolk County Mental Health Project, a first-admission longitudinal cohort study of individuals with psychotic disorders. Participants were recruited from all 12 inpatient psychiatric facilities in Suffolk County, New York. This analysis concerns the 428 participants with at least 2 estimates of general cognitive ability. Data were collected between September 1989 and October 2019, and data were analyzed from January 2020 to October 2021.

Exposures

Psychiatric hospitalization for psychosis.

Main Outcomes and Measures

Preadmission cognitive scores were extracted from school and medical records. Postonset cognitive scores were based on neuropsychological testing at 6-month, 24-month, 20-year, and 25-year follow-ups.

Results

Of the 428 included individuals (212 with schizophrenia and 216 with other psychotic disorders), 254 (59.6%) were male, and the mean (SD) age at psychosis onset was 27 (9) years. Three phases of cognitive change were observed: normative, declining, and deteriorating. In the first phase, cognition was stable. Fourteen years before psychosis onset, those with schizophrenia began to experience cognitive decline at a rate of 0.35 intelligence quotient (IQ) points per year (95% CI, 0.29-0.42; P < .001), a significantly faster decline than those with other psychotic disorders (0.15 IQ points per year; 95% CI, 0.08-0.22, P < .001). At 22 years after onset, both groups declined at a rate of 0.59 IQ points per year (95% CI, 0.25-0.94; P < .001).

Conclusions and Relevance

In this cohort study, cognitive trajectories in schizophrenia were consistent with both a neurodevelopmental and neurodegenerative pattern, resulting in a loss of 16 IQ points over the period of observation. Cognitive decline began long prior to psychosis onset, suggesting the window for primary prevention is earlier than previously thought. A window for secondary prevention emerges in the third decade of illness, when cognitive declines accelerate in individuals with schizophrenia and other psychotic disorders.

A novel longitudinal clustering approach to psychopathology across diagnostic entities in the hospital-based PsyCourse study

Biological research and clinical management in psychiatry face two major impediments: the high degree of overlap in psychopathology between diagnoses and the inherent heterogeneity with regard to severity. Here, we aim to stratify cases into homogeneous transdiagnostic subgroups using psychometric information with the ultimate aim of identifying individuals with higher risk for severe illness. 397 participants of the PsyCourse study with schizophrenia- or bipolar-spectrum diagnoses were prospectively phenotyped over 18 months. Factor analysis of mixed data of different rating scales and subsequent longitudinal clustering were used to cluster disease trajectories. Five clusters of longitudinal trajectories were identified in the psychopathologic dimensions. Clusters differed significantly with regard to Global Assessment of Functioning, disease course, and-in some cases-diagnosis while there were no significant differences regarding sex, age at baseline or onset, duration of illness, or polygenic burden for schizophrenia. Longitudinal clustering may aid in identifying transdiagnostic homogeneous subgroups of individuals with severe psychiatric disease.

Hierarchical Symptom Components in Early Psychosis

BACKGROUND AND HYPOTHESIS

Quantitative models of psychopathology can empirically guide subclassification of heterogeneous clinical presentations such as psychosis; they are particularly well-equipped to capture the nuanced symptomatology observed in first-episode psychosis. As well, components may be better aligned with biological variables. The current study sought to confirm and extend knowledge of the hierarchical structure of psychosis symptoms in first-episode psychosis. Based on past hierarchical work, we hypothesized that a 4 component level would be most closely associated with longitudinal disability.

STUDY DESIGN

Participants with early-stage psychosis (N = 370) underwent clinical assessment with the scale for the assessment of positive symptoms (SAPS), scale for assessment of negative symptoms (SANS), and global assessment scale(GAS). A subset was assessed at 6 months (N = 221) and 1 year (N = 207). Hierarchical symptom components were extracted at 12 levels. The predictive utility of the components for global functioning was tested.

STUDY RESULTS

As predicted, the 4-component model (reality distortion, thought disorder, inexpressivity, apathy/asociality) provided a superior prediction of functioning over other levels of the hierarchy. Baseline apathy/asociality longitudinally predicted functioning beyond the shared variance of the components at 6 months (b = -4.83, t(216) = -5.37, p < .001, R2adj = 0.12) and 1-year (b = -4.49, t(202) = -4.38, p < .001, R2adj = 0.09).

CONCLUSIONS

The hierarchical structure of psychotic symptomatology and its external validity have been robustly established in independent, longitudinal first-episode psychosis samples. The established model incorporates multiple levels of granularity that can be flexibly applied based on the level that offers the greatest predictive utility for external validators.

Association between formal thought disorders, neurocognition and functioning in the early stages of psychosis: a systematic review of the last half-century studies

Recent review articles provided an extensive collection of studies covering many aspects of format thought disorders (FTD) among their epidemiology and phenomenology, their neurobiological underpinnings, genetics as well as their transdiagnostic prevalence. However, less attention has been paid to the association of FTD with neurocognitive and functioning deficits in the early stages of evolving psychosis. Therefore, this systematic review aims to investigate the state of the art regarding the association between FTD, neurocognition and functioning in the early stages of evolving psychotic disorders in adolescents and young adults, by following the PRISMA flowchart. A total of 106 studies were screened. We included 8 studies due to their reports of associations between FTD measures and functioning outcomes measured with different scales and 7 studies due to their reports of associations between FTD measures and neurocognition. In summary, the main findings of the included studies for functioning outcomes showed that FTD severity predicted poor social functioning, unemployment, relapses, re-hospitalisations, whereas the main findings of the included studies for neurocognition showed correlations between attentional deficits, executive functions and FTD, and highlighted the predictive potential of executive dysfunctions for sustained FTD. Further studies in upcoming years taking advantage of the acceleration in computational psychiatry would allow researchers to re-investigate the clinical importance of FTD and their role in the transition from at-risk to full-blown psychosis conditions. Employing automated computer-assisted diagnostic tools in the early stages of psychosis might open new avenues to develop targeted neuropsychotherapeutics specific to FTD.

Converting scores between the PANSS and SAPS/SANS beyond the positive/negative dichotomy

Previous work provided conversion equations for overall indices of positive and negative symptomatology between the Positive and Negative Syndrome Scale (PANSS) and the Scales for the Assessment of Positive/Negative Symptoms (SAPS/SANS). Our objective was to provide such conversion equations for subdomains of positive and negative symptomatology in order to better account for the diversity of symptom profiles in schizophrenia. Symptoms severity was assessed using both the PANSS and SAPS/SANS in 205 patients with schizophrenia. Two exploratory factor analyses combining items from both scales were first performed separately in the positive and negative symptom domains. Positive factors were termed 'Hallucinations', 'Delusions' and 'Disorganization', while negative factors were associated with 'Expressivity', 'Amotivation' and 'Cognition', consistent with current descriptions of symptom dimensions in schizophrenia. For each factor, linear regression analyses were conducted on 80% of the data to obtain conversion equations from the PANSS to the SAPS/SANS and vice versa. Reliability was then evaluated on the 20% remaining data, with good to excellent intra-class correlation coefficients between the original and predicted scores for all but the cognition factor. These findings show that symptom severity scores can be converted with good accuracy between clinical scales beyond the positive/negative symptom dichotomy.

An Effortful Approach to Social Affiliation in Schizophrenia: Preliminary Evidence of Increased Theta and Alpha Connectivity during a Live Social Interaction

People with schizophrenia often experience a profound lack of motivation for social affiliation—a facet of negative symptoms that detrimentally impairs functioning. However, the mechanisms underlying social affiliative deficits remain poorly understood, particularly under realistic social contexts. Here, we investigated subjective reports and electroencephalography (EEG) functional connectivity in schizophrenia during a live social interaction. Individuals with schizophrenia (n = 16) and healthy controls (n = 29) completed a face-to-face interaction with a confederate while having EEG recorded. Participants were randomly assigned to either a Closeness condition designed to elicit feelings of closeness through self-disclosure or a Small-Talk condition with minimal disclosure. Compared to controls, patients reported lower positive emotional experiences and feelings of closeness across conditions, but they showed comparably greater subjective affiliative responses for the Closeness (vs. Small-Talk) condition. Additionally, patients in the Closeness (vs. Small-Talk) condition displayed a global increase in connectivity in theta and alpha frequency bands that was not observed for controls. Importantly, greater theta and alpha connectivity was associated with greater subjective affiliative responding, greater negative symptoms, and lower disorganized symptoms in patients. Collectively, findings indicate that patients, because of pronounced negative symptoms, utilized a less efficient, top-down mediated strategy to process social affiliation.

Fronto-parietal network function during cued visual search in the first-episode schizophrenia spectrum

Cognitive impairments account for significant morbidity in schizophrenia and are present at disease onset. Controlled processes are particularly susceptible and may contribute to pervasive selective attention deficits. The present study assessed fronto-parietal attention network (FPAN) functioning during cue presentation on a visual search task in first-episode schizophrenia spectrum patients (FE) and its relation to symptom burden and community functioning. Brain activity was recorded with magnetoencephalography from 38 FE and 38 healthy controls (HC) during blocks of pop-out and serial search target detection. Activity during cue presentation was compared between groups across bilateral FPAN regions (frontal eye fields (FEF), inferior frontal gyrus (IFG), midcingulate cortex (MCC), and intraparietal sulcus (IPS)). FE exhibited greater right hemisphere IFG activity despite worse performance relative to HC. Performance and FPAN activity were not correlated in HC. Among FE, however, stronger activity within right hemisphere FEF and IFG was associated with faster responses. Stronger right IPS and left IFG activity in patients was also associated with reduced negative symptoms and improved community functioning, respectively. Increased reliance on the FPAN for task completion suggests an inefficient cognitive control network and might reflect a compensation for impaired attentional deployment during target detection, a strategy employed by those with less severe illness. These findings represent a critical step towards identifying the neural substrates of negative symptoms and impaired neurocognition at disease onset.

Pathways from performance monitoring to negative symptoms and functional outcomes in psychotic disorders

BACKGROUND

Performance monitoring entails rapid error detection to maintain task performance. Impaired performance monitoring is a candidate pathophysiological process in psychotic disorders, which may explain the broader deficit in executive function and its known associations with negative symptoms and poor functioning. The current study models cross-sectional pathways bridging neurophysiological measures of performance monitoring with executive function, symptoms, and functioning.

METHODS

Data were from the 20-year assessment of the Suffolk County Mental Health Project. Individuals with psychotic disorders (N = 181) were originally recruited from inpatient psychiatric facilities. Data were also collected from a geographically and demographically matched group with no psychosis history (N = 242). Neural measures were the error-related negativity (ERN) and error positivity (Pe). Structural equation modeling tested mediation pathways.

RESULTS

Blunted ERN and Pe in the clinical cohort related to impaired executive function (r = 0.26-0.35), negative symptom severity (r = 0.17-0.25), and poor real-world functioning (r = 0.17-0.19). Associations with executive function were consistent across groups. Multiple potential pathways were identified in the clinical cohort: reduced ERN to inexpressivity was mediated by executive function (β = 0.10); reduced Pe to global functioning was mediated by executive function and avolition (β = 0.10).

CONCLUSIONS

This supports a transdiagnostic model of psychotic disorders by which poor performance monitoring contributes to impaired executive function, which contributes to negative symptoms and poor real-world functioning. If supported by future longitudinal research, these pathways could inform the development of targeted interventions to address cognitive and functional deficits that are central to psychotic disorders.

Predicting Long-Term Outcomes in First-Admission Psychosis: Does the Hierarchical Taxonomy of Psychopathology Aid DSM in Prognostication?

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical, dimensional model of psychological symptoms and functioning. Its goals are to augment the use and address the limitations of traditional diagnoses, such as arbitrary thresholds of severity, within-disorder heterogeneity, and low reliability. HiTOP has made inroads to addressing these problems, but its prognostic validity is uncertain. The present study sought to test the prediction of long-term outcomes in psychotic disorders was improved when the HiTOP dimensional approach was considered along with traditional (ie, DSM) diagnoses. We analyzed data from the Suffolk County Mental Health Project (N = 316), an epidemiologic study of a first-admission psychosis cohort followed for 20 years. We compared 5 diagnostic groups (schizophrenia/schizoaffective, bipolar disorder with psychosis, major depressive disorder with psychosis, substance-induced psychosis, and other psychoses) and 5 dimensions derived from the HiTOP thought disorder spectrum (reality distortion, disorganization, inexpressivity, avolition, and functional impairment). Both nosologies predicted a significant amount of variance in most outcomes. However, except for cognitive functioning, HiTOP showed consistently greater predictive power across outcomes-it explained 1.7-fold more variance than diagnoses in psychiatric and physical health outcomes, 2.1-fold more variance in community functioning, and 3.4-fold more variance in neural responses. Even when controlling for diagnosis, HiTOP dimensions incrementally predicted almost all outcomes. These findings support a shift away from the exclusive use of categorical diagnoses and toward the incorporation of HiTOP dimensions for better prognostication and linkage with neurobiology.

Extraversion but not depression predicts reward sensitivity: Revisiting the measurement of anhedonic phenotypes

Recently, increasing efforts have been made to define and measure dimensional phenotypes associated with psychiatric disorders. One example is a probabilistic reward task developed by Pizzagalli, Jahn, and O'Shea (2005) to assess anhedonia, by measuring response to a differential reinforcement schedule. This task has been used in many studies, which have connected blunted reward response in the task to depressive symptoms, across clinical groups and in the general population. The current study attempted to replicate these findings in a large community sample and also investigated possible associations with Extraversion, a personality trait linked to reward sensitivity. Participants (N = 299) completed the probabilistic reward task, as well as the Beck Depression Inventory, Personality Inventory for the DSM-5, Big Five Inventory, and Big Five Aspect Scales. Our direct replication attempts used bivariate correlations and analysis of variance models. Follow-up and extension analyses used structural equation models to assess relations among reward sensitivity, depression, Extraversion, and Neuroticism. No significant associations were found between reward sensitivity and depression, thus failing to replicate previous findings. Reward sensitivity (both modeled as response bias aggregated across blocks and as response bias controlling for baseline) showed positive associations with Extraversion, but not Neuroticism. Findings suggest reward sensitivity as measured by this task may be related primarily to Extraversion and its pathological manifestations, rather than to depression per se, consistent with existing models that conceptualize depressive symptoms as combining features of Neuroticism and low Extraversion. Findings are discussed in broader contexts of dimensional psychopathology frameworks, replicable science, and behavioral task reliability. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): II. Externalizing superspectrum

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical effort to address limitations of traditional mental disorder diagnoses. These include arbitrary boundaries between disorder and normality, disorder co-occurrence in the modal case, heterogeneity of presentation within dis-orders, and instability of diagnosis within patients. This paper reviews the evidence on the validity and utility of the disinhibited externalizing and antagonistic externalizing spectra of HiTOP, which together constitute a broad externalizing superspectrum. These spectra are composed of elements subsumed within a variety of mental disorders described in recent DSM nosologies, including most notably substance use disorders and "Cluster B" personality disorders. The externalizing superspectrum ranges from normative levels of impulse control and self-assertion, to maladaptive disinhibition and antagonism, to extensive polysubstance involvement and personality psychopathology. A rich literature supports the validity of the externalizing superspectrum, and the disinhibited and antagonistic spectra. This evidence encompasses common genetic influences, environmental risk factors, childhood antecedents, cognitive abnormalities, neural alterations, and treatment response. The structure of these validators mirrors the structure of the phenotypic externalizing superspectrum, with some correlates more specific to disinhibited or antagonistic spectra, and others relevant to the entire externalizing superspectrum, underlining the hierarchical structure of the domain. Compared with traditional diagnostic categories, the externalizing superspectrum conceptualization shows improved utility, reliability, explanatory capacity, and clinical applicability. The externalizing superspectrum is one aspect of the general approach to psychopathology offered by HiTOP and can make diagnostic classification more useful in both research and the clinic.

A detailed hierarchical model of psychopathology: From individual symptoms up to the general factor of psychopathology

Much of our knowledge about the relationships among domains of psychopathology is built on the diagnostic categories described in the Diagnostic and Statistical Manual of Mental Disorders (DSM), with relatively little research examining the symptom-level structure of psychopathology. The aim of this study was to delineate a detailed hierarchical model of psychopathology-from individual symptoms up to a general factor of psychopathology-allowing both higher- and lower-order dimensions to depart from the structure of the DSM. We explored the hierarchical structure of hundreds of symptoms spanning 18 DSM disorders, in two large samples-one from the general population in Australia (n = 3175), and the other a treatment-seeking clinical sample from the USA (n = 1775). There was marked convergence between the two samples, offering new perspectives on higher-order dimensions of psychopathology. We also found several noteworthy departures from the structure of the DSM in the symptom-level data.

The Hierarchical Taxonomy of Psychopathology (HiTOP): A Quantitative Nosology Based on Consensus of Evidence

Traditional diagnostic systems went beyond empirical evidence on the structure of mental health. Consequently, these diagnoses do not depict psychopathology accurately, and their validity in research and utility in clinicalpractice are therefore limited. The Hierarchical Taxonomy of Psychopathology (HiTOP) consortium proposed a model based on structural evidence. It addresses problems of diagnostic heterogeneity, comorbidity, and unreliability. We review the HiTOP model, supporting evidence, and conceptualization of psychopathology in this hierarchical dimensional framework. The system is not yet comprehensive, and we describe the processes for improving and expanding it. We summarize data on the ability of HiTOP to predict and explain etiology (genetic, environmental, and neurobiological), risk factors, outcomes, and treatment response. We describe progress in the development of HiTOP-based measures and in clinical implementation of the system. Finally, we review outstanding challenges and the research agenda. HiTOP is of practical utility already, and its ongoing development will produce a transformative map of psychopathology.

Psychiatric symptoms and quality of life in older adults with schizophrenia spectrum disorder: results from a multicenter study

The severity of psychopathology has a strong negative impact on quality of life (QoL) among older adults with schizophrenia spectrum disorder. However, because these subjects generally experience multiple psychiatric symptoms, it remains unclear whether decreased QoL in this population is due to specific symptoms (e.g., hallucinations), specific dimensions of psychopathology (e.g., negative symptoms), a general psychopathology dimension representing the shared effect across all psychiatric symptoms, or a combination of these explanations. Data were derived from the Cohort of individuals with Schizophrenia Aged 55 years or more (CSA) study, a large (N = 353) multicenter sample of older adults with schizophrenia spectrum disorder recruited from French public-sector psychiatric departments. We used structural equation modeling to examine the shared and specific effects of psychiatric symptoms on QoL, while adjusting for sociodemographic characteristics, general medical conditions, global cognitive functioning and psychotropic medications. Psychiatric symptoms and QoL were assessed face-to-face by psychiatrists using the Brief Psychiatric Rating Scale (BPRS) and the Quality of Life Scale (QLS). Among older adults with schizophrenia spectrum disorder, effects of psychiatric symptoms on QoL were exerted mostly through a general psychopathology dimension (β = - 0.43, p < 0.01). Negative symptom dimension had an additional negative effect on QoL beyond the effect of that factor (β = - 0.28, p < 0.01). Because psychiatric symptoms affect QoL mainly through two dimensions of psychopathology, i.e., a general psychopathology dimension and a negative symptom dimension, mechanisms underlying those dimensions should be considered as promising targets for therapeutic interventions to substantially improve quality of life of this vulnerable population.

Psychosis Risk and Development: What Do We Know From Population-Based Studies?

Recent years have seen an advent in population-based studies in children, adolescents, and adults that examine the prevalence, etiology, and developmental trajectories of diverse subclinical psychopathological symptoms that pose a risk for the later development of severe mental illnesses. It is increasingly recognized that most categorically defined psychiatric disorders occur on a spectrum or continuum, show high heterogeneity and symptom overlap, and share genetic and environmental risk factors. We discuss neurodevelopmental underpinnings of psychosis spectrum symptoms and review brain morphometric and functional alterations as well as genetic liability for psychosis in individuals experiencing psychotic symptoms (PSs) in the general population. With regard to brain structure and function, findings of qualitatively similar alterations in individuals experiencing subthreshold PSs and individuals with overt psychotic disorders support the notion of a psychosis continuum. However, genetic and epidemiological studies have emphasized the overlap of PSs and other psychiatric illnesses. In particular, PSs during adolescence appear to be a nonspecific precursor of different psychopathological outcomes. Given the evidence presented in this review, we argue that findings from population-based studies are appropriate to guide policy-making to further emphasize public health efforts. Broadly accessible mental health programs are promising to make a difference in the field of adolescent mental health. However, the specific efficacy of these programs warrants further study, and caution is advised to not overpathologize potentially transient occurrence of mental health problems.

Latent Clinical-Anatomical Dimensions of Schizophrenia

Widespread structural brain abnormalities have been consistently reported in schizophrenia, but their relation to the heterogeneous clinical manifestations remains unknown. In particular, it is unclear whether anatomical abnormalities in discrete regions give rise to discrete symptoms or whether distributed abnormalities give rise to the broad clinical profile associated with schizophrenia. Here, we apply a multivariate data-driven approach to investigate covariance patterns between multiple-symptom domains and distributed brain abnormalities in schizophrenia. Structural magnetic resonance imaging and clinical data were derived from one discovery sample (133 patients and 113 controls) and one independent validation sample (108 patients and 69 controls). Disease-related voxel-wise brain abnormalities were estimated using deformation-based morphometry. Partial least-squares analysis was used to comprehensively map clinical, neuropsychological, and demographic data onto distributed deformation in a single multivariate model. The analysis identified 3 latent clinical-anatomical dimensions that collectively accounted for 55% of the covariance between clinical data and brain deformation. The first latent clinical-anatomical dimension was replicated in an independent sample, encompassing cognitive impairments, negative symptom severity, and brain abnormalities within the default mode and visual networks. This cognitive-negative dimension was associated with low socioeconomic status and was represented across multiple races. Altogether, we identified a continuous cognitive-negative dimension of schizophrenia, centered on 2 intrinsic networks. By simultaneously taking into account both clinical manifestations and neuroanatomical abnormalities, the present results open new avenues for multi-omic stratification and biotyping of individuals with schizophrenia.

Associations of mismatch negativity with psychotic symptoms and functioning transdiagnostically across psychotic disorders

Mismatch negativity (MMN) amplitude has been widely shown to be diminished in schizophrenia and, more recently, in other psychotic disorders. Although there is considerable evidence linking MMN reduction to cognitive and functional deficits in schizophrenia, there is little evidence of associations with specific psychotic symptoms. Further, it is unclear if MMN reductions relate to specific symptoms, cognitive, and functional deficits transdiagnostically across different psychotic disorders. The present study examines MMN amplitude in a large cohort of cases diagnosed with psychotic disorders including schizophrenia and schizoaffective disorder (N = 116); bipolar disorder and major depressive disorder (N = 75); and other psychotic disorders (N = 25), as well as individuals with no psychotic disorder diagnoses (N = 248). Furthermore, we examined the association of MMN with symptoms, cognitive functioning, and real-world functioning to determine whether these relationships differ by diagnosis. Results showed that MMN amplitude was reduced in cases overall compared to never-psychotic individuals, with no differences between psychotic disorders. Furthermore, there were transdiagnostic associations of reduced duration MMN (MMN-D) with worse auditory hallucinations (r = .14) and disorganization (r = .14), frequency MMN (MMN-F) with real-word functioning (r = .20) and episodic memory (r = -.22), and both components with executive functioning (MMN-D: r = -.17; MMN-F: r = -.15). Our findings relating MMN reductions with cognitive and real-world functioning replicate earlier research in schizophrenia and extend these relationships to other psychotic disorders. Furthermore, our correlations with MMN-D are consistent with computational modeling research and theoretical proposals that view MMN reduction, cognitive dysfunction, and psychotic symptoms as reflecting underlying predictive coding deficits. However, differences in relationships with MMN-F suggest that additional work is warranted on this topic. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): I. Psychosis superspectrum

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a scientific effort to address shortcomings of traditional mental disorder diagnoses, which suffer from arbitrary boundaries between psychopathology and normality, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. This paper synthesizes evidence on the validity and utility of the thought disorder and detachment spectra of HiTOP. These spectra are composed of symptoms and maladaptive traits currently subsumed within schizophrenia, other psychotic disorders, and schizotypal, paranoid and schizoid personality disorders. Thought disorder ranges from normal reality testing, to maladaptive trait psychoticism, to hallucinations and delusions. Detachment ranges from introversion, to maladaptive detachment, to blunted affect and avolition. Extensive evidence supports the validity of thought disorder and detachment spectra, as each spectrum reflects common genetics, environmental risk factors, childhood antecedents, cognitive abnormalities, neural alterations, biomarkers, and treatment response. Some of these characteristics are specific to one spectrum and others are shared, suggesting the existence of an overarching psychosis superspectrum. Further research is needed to extend this model, such as clarifying whether mania and dissociation belong to thought disorder, and explicating processes that drive development of the spectra and their subdimensions. Compared to traditional diagnoses, the thought disorder and detachment spectra demonstrated substantially improved utility: greater reliability, larger explanatory and predictive power, and higher acceptability to clinicians. Validated measures are available to implement the system in practice. The more informative, reliable and valid characterization of psychosis-related psychopathology offered by HiTOP can make diagnosis more useful for research and clinical care.

An Investigation of Psychosis Subgroups With Prognostic Validation and Exploration of Genetic Underpinnings: The PsyCourse Study

IMPORTANCE

Identifying psychosis subgroups could improve clinical and research precision. Research has focused on symptom subgroups, but there is a need to consider a broader clinical spectrum, disentangle illness trajectories, and investigate genetic associations.

OBJECTIVE

To detect psychosis subgroups using data-driven methods and examine their illness courses over 1.5 years and polygenic scores for schizophrenia, bipolar disorder, major depression disorder, and educational achievement.

DESIGN, SETTING, AND PARTICIPANTS

This ongoing multisite, naturalistic, longitudinal (6-month intervals) cohort study began in January 2012 across 18 sites. Data from a referred sample of 1223 individuals (765 in the discovery sample and 458 in the validation sample) with DSM-IV diagnoses of schizophrenia, bipolar affective disorder (I/II), schizoaffective disorder, schizophreniform disorder, and brief psychotic disorder were collected from secondary and tertiary care sites. Discovery data were extracted in September 2016 and analyzed from November 2016 to January 2018, and prospective validation data were extracted in October 2018 and analyzed from January to May 2019.

MAIN OUTCOMES AND MEASURES

A clinical battery of 188 variables measuring demographic characteristics, clinical history, symptoms, functioning, and cognition was decomposed using nonnegative matrix factorization clustering. Subtype-specific illness courses were compared with mixed models and polygenic scores with analysis of covariance. Supervised learning was used to replicate results in validation data with the most reliably discriminative 45 variables.

RESULTS

Of the 765 individuals in the discovery sample, 341 (44.6%) were women, and the mean (SD) age was 42.7 (12.9) years. Five subgroups were found and labeled as affective psychosis (n = 252), suicidal psychosis (n = 44), depressive psychosis (n = 131), high-functioning psychosis (n = 252), and severe psychosis (n = 86). Illness courses with significant quadratic interaction terms were found for psychosis symptoms (R2 = 0.41; 95% CI, 0.38-0.44), depression symptoms (R2 = 0.28; 95% CI, 0.25-0.32), global functioning (R2 = 0.16; 95% CI, 0.14-0.20), and quality of life (R2 = 0.20; 95% CI, 0.17-0.23). The depressive and severe psychosis subgroups exhibited the lowest functioning and quadratic illness courses with partial recovery followed by reoccurrence of severe illness. Differences were found for educational attainment polygenic scores (mean [SD] partial η2 = 0.014 [0.003]) but not for diagnostic polygenic risk. Results were largely replicated in the validation cohort.

CONCLUSIONS AND RELEVANCE

Psychosis subgroups were detected with distinctive clinical signatures and illness courses and specificity for a nondiagnostic genetic marker. New data-driven clinical approaches are important for future psychosis taxonomies. The findings suggest a need to consider short-term to medium-term service provision to restore functioning in patients stratified into the depressive and severe psychosis subgroups.

Toward a Neural Model of the Openness-Psychoticism Dimension: Functional Connectivity in the Default and Frontoparietal Control Networks

Psychosis proneness has been linked to heightened Openness to Experience and to cognitive deficits. Openness and psychotic disorders are associated with the default and frontoparietal networks, and the latter network is also robustly associated with intelligence. We tested the hypothesis that functional connectivity of the default and frontoparietal networks is a neural correlate of the openness-psychoticism dimension. Participants in the Human Connectome Project (N = 1003) completed measures of psychoticism, openness, and intelligence. Resting state functional magnetic resonance imaging was used to identify intrinsic connectivity networks. Structural equation modeling revealed relations among personality, intelligence, and network coherence. Psychoticism, openness, and especially their shared variance were related positively to default network coherence and negatively to frontoparietal coherence. These associations remained after controlling for intelligence. Intelligence was positively related to frontoparietal coherence. Research suggests that psychoticism and openness are linked in part through their association with connectivity in networks involving experiential simulation and cognitive control. We propose a model of psychosis risk that highlights roles of the default and frontoparietal networks. Findings echo research on functional connectivity in psychosis patients, suggesting shared mechanisms across the personality-psychopathology continuum.

The Symptom and Domain Structure of Psychotic Disorders: A Network Analysis Approach

Little is understood about the symptom network structure of psychotic disorders. In the current study, we aimed to examine the network structure of psychotic symptoms in a broad and transdiagnostic sample of subjects with psychotic disorders (n = 2240) and to determine whether network structure parameters vary across demographic, sampling method and clinical variables. Gaussian graphical models were estimated for 73 psychotic symptoms assessed using the Comprehensive Assessment of Symptoms and History. A 7-cluster solution (reality distortion, disorganization, catatonia, diminished expressivity, avolition/anhedonia, mania, and depression) best explained the underlying symptom structure of the network. Symptoms with the highest centrality estimates pertained to the disorganization and, to a lesser extent, negative domains. Most bridge symptoms pertained to the disorganization domain, which had a central position within the network and widespread connections with other psychopathological domains. A comparison of networks in subgroups of subjects defined by premorbid adjustment levels, treatment response, and course pattern significantly influenced both network global strength and network structure. The sampling method and diagnostic class influenced network structure but not network global strength. Subgroups of subjects with less densely connected networks had poorer outcomes or more illness severity than those with more densely connected networks. The network structure of psychotic features emphasizes the importance of disorganization symptoms as a central domain of psychopathology and raises the possibility that interventions that target these symptoms may prove of broad use across psychopathology. The network structure of psychotic disorders is dependent on the sampling method and important clinical variables.

Long-term Changes in Cognitive Functioning in Individuals With Psychotic Disorders: Findings From the Suffolk County Mental Health Project

IMPORTANCE

It remains uncertain whether people with psychotic disorders experience progressive cognitive decline or normal cognitive aging after first hospitalization. This information is essential for prognostication in clinical settings, deployment of cognitive remediation, and public health policy.

OBJECTIVE

To examine long-term cognitive changes in individuals with psychotic disorders and to compare age-related differences in cognitive performance between people with psychotic disorders and matched control individuals (ie, individuals who had never had psychotic disorders).

DESIGN, SETTING, AND PARTICIPANTS

The Suffolk County Mental Health Project is an inception cohort study of first-admission patients with psychosis. Cognitive functioning was assessed 2 and 20 years later. Patients were recruited from the 12 inpatient facilities of Suffolk County, New York. At year 20, the control group was recruited by random digit dialing and matched to the clinical cohort on zip code and demographics. Data were collected between September 1991 and July 2015. Analysis began January 2016.

MAIN OUTCOMES AND MEASURES

Change in cognitive functioning in 6 domains: verbal knowledge (Wechsler Adult Intelligence Scale-Revised vocabulary test), verbal declarative memory (Verbal Paired Associates test I and II), visual declarative memory (Visual Reproduction test I and II), attention and processing speed (Symbol Digit Modalities Test-written and oral; Trail Making Test [TMT]-A), abstraction-executive function (Trenerry Stroop Color Word Test; TMT-B), and verbal fluency (Controlled Oral Word Association Test).

RESULTS

A total of 705 participants were included in the analyses (mean [SD] age at year 20, 49.4 [10.1] years): 445 individuals (63.1%) had psychotic disorders (211 with schizophrenia spectrum [138 (65%) male]; 164 with affective psychoses [76 (46%) male]; 70 with other psychoses [43 (61%) male]); and 260 individuals (36.9%) in the control group (50.5 [9.0] years; 134 [51.5%] male). Cognition in individuals with a psychotic disorder declined on all but 2 tests (average decline: d = 0.31; range, 0.17-0.54; all P < .001). Cognitive declines were associated with worsening vocational functioning (Visual Reproduction test II: r = 0.20; Symbol Digit Modalities Test-written: r = 0.25; Stroop: r = 0.24; P < .009) and worsening negative symptoms (avolition: Symbol Digit Modalities Test-written: r = -0.24; TMT-A: r = -0.21; Stroop: r = -0.21; all P < .009; inexpressivity: Stroop: r = -0.22; P < .009). Compared with control individuals, people with psychotic disrders showed age-dependent deficits in verbal knowledge, fluency, and abstraction-executive function (vocabulary: β = -0.32; Controlled Oral Word Association Test: β = -0.32; TMT-B: β = 0.23; all P < .05), with the largest gap among participants 50 years or older.

CONCLUSIONS AND RELEVANCE

In individuals with psychotic disorders, most cognitive functions declined over 2 decades after first hospitalization. Observed declines were clinically significant. Some declines were larger than expected due to normal aging, suggesting that cognitive aging in some domains may be accelerated in this population. If confirmed, these findings would highlight cognition as an important target for research and treatment during later phases of psychotic illness.

Interfacing neural constructs with the Hierarchical Taxonomy of Psychopathology: 'Why' and 'how'

The Hierarchical Taxonomy of Psychopathology (HiTOP) represents a crucial step forward in the empirical refinement of psychiatric nosology. Although grounded in factor analyses of clinical symptoms and affiliated traits, HiTOP encourages research using measures of other types, including neural-system variables, to clarify coherent processes contributing to the hierarchical structure of psychopathology. However, systematic strategies for interfacing HiTOP dimensions with neural-system variables have not been put forth. We discuss reasons for considering neurobiological systems in relation to HiTOP (i.e. 'why') and propose alternative strategies that might be used to develop an interface between HiTOP and neurobiology (i.e. 'how'). In particular, we highlight potential advantages and limitations of establishing this interface through reference to (i) HiTOP dimensions themselves, or conventional personality trait models linked to HiTOP; (ii) alternative trait constructs designed to link conventional personality models and neurobiological measures; and (iii) mechanistic models of neurobiological processes relevant to HiTOP constructs, derived from computational modelling. We discuss the importance of establishing an interface between HiTOP and neurobiology to develop a more comprehensive, mechanistic understanding of psychopathology and to guide the refinement of the HiTOP model. Such efforts have the potential to guide the development and provision of effective, individualized psychological treatment.

Transdiagnostic Dimensions of Psychiatric Comorbidity in Individuals at Clinical High Risk for Psychosis: A Preliminary Study Informed by HiTOP

Background: Although psychiatric comorbidity is the norm among individuals at clinical high risk for psychotic disorders (CHR), research has yet to examine transdiagnostic dimensional models of comorbidity in this critical population. Methods: This study analyzed quantitative measures of eleven psychiatric syndromes in a group at CHR (n = 71) and a matched healthy comparison group (n = 73) to determine these syndromes' dimensional structure and relationships to cognition, functioning, and risk of conversion to psychotic disorders. Results: Relative to the comparison group, the CHR group was elevated on all eleven psychiatric syndromes. Exploratory factor analysis found three psychopathology dimensions: internalizing, negative symptoms, and positive symptoms. Depression cross-loaded onto the internalizing and negative symptom dimensions. Hypomania loaded positively on positive symptoms but negatively on negative symptoms. The negative symptom factor was associated with poorer cognition and functioning and a higher risk of conversion to psychosis. Conclusions: These dimensions align with internalizing, detachment, and thought disorder, three of the five spectra in higher-order models such as the Hierarchical Taxonomy of Psychopathology (HiTOP). In the CHR state, detachment appears to be particularly insidious and predictive of psychosis. Further research is required to distinguish depression and hypomania from attenuated psychotic symptoms in this population.

Schizophrenia polygenic risk score and 20-year course of illness in psychotic disorders

Understanding whether and how the schizophrenia polygenic risk score (SZ PRS) predicts course of illness could improve diagnosis and prognostication in psychotic disorders. We tested whether the SZ PRS predicts symptoms, cognition, illness severity, and diagnostic changes over the 20 years following first admission. The Suffolk County Mental Health Project is an inception cohort study of first-admission patients with psychosis. Patients were assessed six times over 20 years, and 249 provided DNA. Geographically- and demographically-matched never psychotic adults were recruited at year 20, and 205 provided DNA. Symptoms were rated using the Schedule for the Assessment of Positive Symptoms and Schedule for the Assessment of Negative Symptoms. Cognition was evaluated with a comprehensive neuropsychological battery. Illness severity and diagnosis were determined by consensus of study psychiatrists. SZ PRS was significantly higher in first-admission than never psychotic groups. Within the psychosis cohort, the SZ PRS predicted more severe negative symptoms (β = 0.21), greater illness severity (β = 0.28), and worse cognition (β = -0.35), across the follow-up. The SZ PRS was the strongest predictor of diagnostic shifts from affective to non-affective psychosis over the 20 years (AUC = 0.62). The SZ PRS predicts persistent differences in cognition and negative symptoms. The SZ PRS also predicts who among those who appear to have a mood disorder with psychosis at first admission will ultimately be diagnosed with a schizophrenia spectrum disorder. These findings show potential for the SZ PRS to become a tool for diagnosis and treatment planning.

Delineating and validating higher-order dimensions of psychopathology in the Adolescent Brain Cognitive Development (ABCD) study

Hierarchical dimensional systems of psychopathology promise more informative descriptions for understanding risk and predicting outcome than traditional diagnostic systems, but it is unclear how many major dimensions they should include. We delineated the hierarchy of childhood and adult psychopathology and validated it against clinically relevant measures. Participants were 9987 9- and 10-year-old children and their parents from the Adolescent Brain Cognitive Development (ABCD) study. Factor analyses of items from the Child Behavior Checklist and Adult Self-Report were run to delineate hierarchies of dimensions. We examined the familial aggregation of the psychopathology dimensions, and the ability of different factor solutions to account for risk factors, real-world functioning, cognitive functioning, and physical and mental health service utilization. A hierarchical structure with a general psychopathology ('p') factor at the apex and five specific factors (internalizing, somatoform, detachment, neurodevelopmental, and externalizing) emerged in children. Five similar dimensions emerged also in the parents. Child and parent p-factors correlated highly (r = 0.61, p < 0.001), and smaller but significant correlations emerged for convergent dimensions between parents and children after controlling for p-factors (r = 0.09-0.21, p < 0.001). A model with child p-factor alone explained mental health service utilization (R² = 0.23, p < 0.001), but up to five dimensions provided incremental validity to account for developmental risk and current functioning in children (R² = 0.03-0.19, p < 0.001). In this first investigation comprehensively mapping the psychopathology hierarchy in children and adults, we delineated a hierarchy of higher-order dimensions associated with a range of clinically relevant validators. These findings hold important implications for psychiatric nosology and future research in this sample.

Common Taxonomy of Traits and Symptoms: Linking Schizophrenia Symptoms, Schizotypy, and Normal Personality

The associations among normal personality and many mental disorders are well established, but it remains unclear whether and how symptoms of schizophrenia and schizotypal traits align with the personality taxonomy. This study examined the joint factor structure of normal personality, schizotypy, and schizophrenia symptoms in people with psychotic disorders (n = 288) and never-psychotic adults (n = 257) in the Suffolk County Mental Health Project. First, we evaluated the structure of schizotypal (positive schizotypy, negative schizotypy, and mistrust) and normal traits. In both the psychotic-disorder and never-psychotic groups, the best-fitting model had 5 factors: neuroticism, extraversion, conscientiousness, agreeableness, and psychoticism. The schizotypy traits were placed on different dimensions: negative schizotypy went on (low) extraversion, whereas positive schizotypy and mistrust went on psychoticism. Next, we added symptoms to the model. Numerous alternatives were compared, and the 5-factor model remained best-fitting. Reality distortion (hallucinations and delusions) and disorganization symptoms were placed on psychoticism, and negative symptoms were placed on extraversion. Models that separated symptom dimensions from trait dimensions did not fit well, arguing that taxonomies of symptoms and traits are aligned. This is the first study to show that symptoms of psychosis, schizotypy, and normal personality reflect the same underlying dimensions. Specifically, (low) extraversion, negative schizotypy, and negative symptoms form one spectrum, whereas psychoticism, positive schizotypy, and positive and disorganized symptoms form another. This framework helps to understand the heterogeneity of psychosis and comorbidity patterns found in psychotic disorders. It also underscores the importance of traits to understanding these disorders.

Symptom-level analysis of DSM-IV/DSM-5 personality pathology in later life: Hierarchical structure and predictive validity across self- and informant ratings

Dissatisfaction with the categorical model of personality disorder led to several investigations on alternative, dimensional systems. The majority of these studies were conducted at the syndrome-level where each diagnostic criterion is summed or averaged within each disorder. Studies at the symptom-level have identified symptom dimensions that define and cut across categories, but the number and nature of dimensions varies across studies. The purpose of the present study was to examine the hierarchical structure and impact of personality pathology at the symptom-level across self- and informant ratings in a large community sample of older adults (N = 1,630; ages 55 to 64). Results indicated that multiple structural patterns can be organized within a common hierarchical framework, with a general factor of maladjustment at the top, 2 broad dimensions of internalizing and externalizing pathology directly below, and progressively more specific symptom dimensions toward the bottom. Factors at each level of the hierarchy were similar across self- and informant ratings. The 4-factor model showed significant incremental validity in predicting a range of life outcomes over simpler models, while increasingly complex models incrementally but modestly improved predictive power. Several consistencies emerged between the current findings and prior factor analytic studies. The most unexpected result was the conspicuous absence of a disinhibition factor reflecting antisocial and impulsivity-related problems. This anomaly may involve the older age of our sample and the changing expression of personality pathology in later life. (PsycINFO Database Record (c) 2019 APA, all rights reserved).