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Introduction. Bankruptcy is the most important element of legal regulation of modern market relations. National economy has to be able to predict a potential default in the general system of anti-crisis management. Therefore, it needs advanced techniques and tools of anti-crisis diagnostics for the timely management solutions. Study objects and methods. The analytical information presented in this work is multi-tiered and reflects the all-Russian, industrywide, regional, regional-industry, and corporate levels. The research featured agricultural enterprises of the Kemerovo region. The information underwent three types of formatting: legislative, statistical, and diagnostic. Results and discussion. During the first stage, the authors assessed external factors and trends in individual components of anti-crisis diagnostics in a given economy sector against the background of all-Russian and industry-wide trends. Enterprises appeared sensitive to bankruptcy risk; the trend decreased in 2014–2018. The second stage involved developing of a selective-indicative model for diagnosing insolvency of Russian organizations. The model took into account regional and industrial traits and focuses on large and medium-sized agricultural enterprises in the region. The model selected general indicators from a set of studied parameters, formed from fifty financial ratios presented in twenty-two of the most well-known methods of anti-crisis analysis. Bankruptcy was diagnosed on the basis of preference matrix, according to the criterion of the active use of coefficients in analytical practice. A comparative analysis of bankruptcy criteria and indicators made it possible to define the degree of adequacy of the set of indicators. Four analytical vectors were defined after thematic grouping of the identified indicators: balance sheet liquidity (current liquidity ratio), property and capital structure (financial dependence and asset mobility ratios), security (working capital ratio with own circulating assets), efficiency (economic profitability, or loss ratio, and the ratio of business activity in the market). The equation of rating assessment of the insolvency probability demonstrated the total impact of these indicators, taking into account their individual «equity participation» in the aggregate of key parameters. Conclusion. The final set of general exponents of the diagnostic model can be qualified as a neuro-analogue of «classical» models that ignores the values of the regression coefficients, which are usually not adapted to Russian realities. The model built on the basis of bankruptcy indicators, taking into account their individual «equity participation» in the rating number, can be used as a flexible methodological tool for diagnosing bankruptcy in the national economy of Russia.

Evaluating the performance of university faculty is a hard task because of the diversity of the work performed. The authors assume that the founder of the university evaluates the effectiveness of the university according to performing the teaching staff. The aim of the study is to improve the monitoring of key performance indicators of the university teaching staff based on data management. The authors present an information system to support decision-making related to the teaching staff at Kemerovo State University. The authors of the paper describe creating such a system using Business Intelligence technologies step by step. The authors identified data sources, designed the structure of the data mart and built ETL-processes for its filling, implemented various analytical dashboards. Implementing the information system in the daily activities of the university allows responding promptly to changes in the key indicators, forecasting their further change, deciding on activation of efforts in the chosen direction or types of work.

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.

The Hierarchical Taxonomy of Psychopathology consortium aims to develop a comprehensive self-report measure to assess psychopathology dimensionally. The current research describes the initial conceptualization, development, and item selection for the thought disorder spectrum and related constructs from other spectra. The thought disorder spectrum is defined primarily by the positive and disorganized traits and symptoms of schizophrenia-spectrum disorders. The Thought Disorder Sub-Workgroup identified and defined 16 relevant constructs and wrote 10 to 15 items per each construct. These items were administered, along with detachment and mania items, to undergraduates and people with serious mental illness. Three hundred and sixty-five items across 25 scales were administered. An exploratory factor analysis of the scale scores suggested a two-factor structure corresponding to positive and negative symptoms for two samples. The mania scales loaded with the positive factor, while the detachment scales loaded with the negative factor. Item-level analyses resulted in 19 preliminary scales, including 215 items that cover the range of thought disorder pathology, and will be carried forward for the next phase of data collection/analysis.

INTRODUCTION: Performance-based assessments of social skills have detected impairments in people with severe mental illness and are correlated with functional outcomes in people with schizophrenia and bipolar disorder. The most common of these assessments, the Social Skills Performance Assessment (SSPA), has two communication scenarios and items measuring both social competence and appropriateness. As real-world competence and appropriateness appear to have different correlates, we hypothesized that SSPA Items measuring competence and appropriateness would be distinct and have different correlations with other outcomes. METHODS: We aggregated data from 557 people with schizophrenia, 106 with bipolar disorder, and 378 well controls from 4 separate research studies. All participants were assessed with both SSPA scenarios and other performance based and clinician-rated measures. A single expert rated the SSPA interactions for competence and appropriateness while blind to participant diagnoses. RESULTS: Participants with bipolar disorder and schizophrenia performed more poorly on every item of the SSPA than healthy controls. Items measuring social competence and appropriateness in communication were intercorrelated across scenarios, as were elements of socially competent communication, although the items measuring competence did not correlate substantially with appropriateness. Items assessing social competence, but not social appropriateness, correlated with better cognitive and functional performance and residential and financial independence. DISCUSSION: Social competence and social appropriateness were distinct elements of performance-based social skills with potential differences in their functional correlates. As both social competence and appropriateness impact functional outcomes, improvement in the measurement and treatment of appropriate communication seems to be an important goal.

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.

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.

Schizophrenia is a common, chronic and debilitating neuropsychiatric syndrome affecting tens of millions of individuals worldwide. While rare genetic variants play a role in the etiology of schizophrenia, most of the currently explained liability is within common variation, suggesting that variation predating the human diaspora out of Africa harbors a large fraction of the common variant attributable heritability. However, common variant association studies in schizophrenia have concentrated mainly on cohorts of European descent. We describe genome-wide association studies of 6152 cases and 3918 controls of admixed African ancestry, and of 1234 cases and 3090 controls of Latino ancestry, representing the largest such study in these populations to date. Combining results from the samples with African ancestry with summary statistics from the Psychiatric Genomics Consortium (PGC) study of schizophrenia yielded seven newly genome-wide significant loci, and we identified an additional eight loci by incorporating the results from samples with Latino ancestry. Leveraging population differences in patterns of linkage disequilibrium, we achieve improved fine-mapping resolution at 22 previously reported and 4 newly significant loci. Polygenic risk score profiling revealed improved prediction based on trans-ancestry meta-analysis results for admixed African (Nagelkerke's R2 = 0.032; liability R2 = 0.017; P

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical structural model of psychological symptoms formulated to improve the reliability and validity of clinical assessment. Neurobiology can inform assessments of early risk and intervention strategies, and the HiTOP model has greater potential to interface with neurobiological measures than traditional categorical diagnoses given its enhanced reliability. However, one complication is that observed biological correlates of clinical symptoms can reflect various factors, ranging from dispositional risk to consequences of psychopathology. In this paper, we argue that the HiTOP model provides an optimized framework for conducting research on the biological correlates of psychopathology from an ontogenetic perspective that distinguishes among indicators of liability, current symptoms, and consequences of illness. Through this approach, neurobiological research can contribute more effectively to identifying individuals at high dispositional risk, indexing treatment-related gains, and monitoring the consequences of mental illness, consistent with the aims of the HiTOP framework.
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Genetic discovery in psychiatry and clinical psychology is hindered by suboptimal phenotypic definitions. We argue that the hierarchical, dimensional, and data-driven classification system proposed by the Hierarchical Taxonomy of Psychopathology (HiTOP) consortium provides a more effective approach to identifying genes that underlie mental disorders, and to studying psychiatric etiology, than current diagnostic categories. Specifically, genes are expected to operate at different levels of the HiTOP hierarchy, with some highly pleiotropic genes influencing higher order psychopathology (e.g., the general factor), whereas other genes conferring more specific risk for individual spectra (e.g., internalizing), subfactors (e.g., fear disorders), or narrow symptoms (e.g., mood instability). We propose that the HiTOP model aligns well with the current understanding of the higher order genetic structure of psychopathology that has emerged from a large body of family and twin studies. We also discuss the convergence between the HiTOP model and findings from recent molecular studies of psychopathology indicating broad genetic pleiotropy, such as cross-disorder SNP-based shared genetic covariance and polygenic risk scores, and we highlight molecular genetic studies that have successfully redefined phenotypes to enhance precision and statistical power. Finally, we suggest how to integrate a HiTOP approach into future molecular genetic research, including quantitative and hierarchical assessment tools for future data-collection and recommendations concerning phenotypic analyses. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

ObjectiveDiagnosis is a cornerstone of clinical practice for mental health care providers, yet traditional diagnostic systems have well-known shortcomings, including inadequate reliability, high comorbidity, and marked within-diagnosis heterogeneity. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a data-driven, hierarchically based alternative to traditional classifications that conceptualizes psychopathology as a set of dimensions organized into increasingly broad, transdiagnostic spectra. Prior work has shown that using a dimensional approach improves reliability and validity, but translating a model like HiTOP into a workable system that is useful for health care providers remains a major challenge.MethodThe present work outlines the HiTOP model and describes the core principles to guide its integration into clinical practice.ResultsPotential advantages and limitations of the HiTOP model for clinical utility are reviewed, including with respect to case conceptualization and treatment planning. A HiTOP approach to practice is illustrated and contrasted with an approach based on traditional nosology. Common barriers to using HiTOP in real-world health care settings and solutions to these barriers are discussed.ConclusionsHiTOP represents a viable alternative to classifying mental illness that can be integrated into practice today, although research is needed to further establish its utility. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The categorical model of personality disorder classification in the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders (5th ed. [DSM-5]; American Psychiatric Association, 2013 ) is highly and fundamentally problematic. Proposed for DSM-5 and provided within Section III (for Emerging Measures and Models) was the Alternative Model of Personality Disorder (AMPD) classification, consisting of Criterion A (self-interpersonal deficits) and Criterion B (maladaptive personality traits). A proposed alternative to the DSM-5 more generally is an empirically based dimensional organization of psychopathology identified as the Hierarchical Taxonomy of Psychopathology (HiTOP; Kotov et al., 2017 ). HiTOP currently includes, at the highest level, a general factor of psychopathology. Further down are the five domains of detachment, antagonistic externalizing, disinhibited externalizing, thought disorder, and internalizing (along with a provisional sixth somatoform dimension) that align with Criterion B. The purpose of this article is to discuss the potential inclusion and placement of the self-interpersonal deficits of the DSM-5 Section III Criterion A within HiTOP.

For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system-the Hierarchical Taxonomy of Psychopathology (HiTOP)-that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across diverse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness.

Objective: Midbrain dopamine function plays a key role in translational models of substance use disorders. Whether midbrain dopamine function is associated with substance use frequency and severity or reward function in 20–24 year-olds remains a critical gap in knowledge. The authors collected neuromelanin-sensitive magnetic resonance imaging (NM-MRI), a validated index of lifetime dopamine function in the substantia nigra/ventral tegmentum area (SN-VTA) complex, to characterize altered dopamine function. Method: Midbrain NM-MRI contrast-to-noise ratio (CNR) was acquired in 135 20–24 year-olds (105 women and 30 men). A composite measure of cumulative substance use was derived from factor analysis of lifetime alcohol intoxications, lifetime cannabis use, use of nicotine in heaviest month, number of classes of drugs used, and ever meeting DSM-5 criteria for a SUD. Trait reward function was assessed by self-report. Results: Cumulative substance use was significantly positively associated with NM-MRI CNR in a large area of the bilateral SN-VTA complex, an effect which was driven by women (who comprised most of the sample) and by voxels with greater NM-MRI CNR, including the ventral tegmentum area. NM-MRI CNR was not associated with individual differences in trait reward function. Conclusions: History of substance use is associated with greater NM signal in NM-rich areas of the midbrain, especially in women. Future longitudinal studies with repeated NM-MRI assessments, especially in younger cohorts and while including more men, are warranted to evaluate whether aberrant dopamine function predates, follows, or is modulated by substance use. [ABSTRACT FROM AUTHOR]

The latent structure of schizotypy and psychosis-spectrum symptoms remains poorly understood. Furthermore, molecular genetic substrates are poorly defined, largely due to the substantial resources required to collect rich phenotypic data across diverse populations. Sample sizes of phenotypic studies are often insufficient for advanced structural equation modeling approaches. In the last 50 years, efforts in both psychiatry and psychological science have moved toward (1) a dimensional model of psychopathology (eg, the current Hierarchical Taxonomy of Psychopathology [HiTOP] initiative), (2) an integration of methods and measures across traits and units of analysis (eg, the RDoC initiative), and (3) powerful, impactful study designs maximizing sample size to detect subtle genomic variation relating to complex traits (the Psychiatric Genomics Consortium [PGC]). These movements are important to the future study of the psychosis spectrum, and to resolving heterogeneity with respect to instrument and population. The International Consortium of Schizotypy Research is composed of over 40 laboratories in 12 countries, and to date, members have compiled a body of schizotypy- and psychosis-related phenotype data from more than 30000 individuals. It has become apparent that compiling data into a protected, relational database and crowdsourcing analytic and data science expertise will result in significant enhancement of current research on the structure and biological substrates of the psychosis spectrum. The authors present a data-sharing infrastructure similar to that of the PGC, and a resource-sharing infrastructure similar to that of HiTOP. This report details the rationale and benefits of the phenotypic data collective and presents an open invitation for participation.