The Research Domain Criteria (RDoC) and the historical roots of psychopathology: A viewpoint

Dissociative Model in Patients With Resistant Schizophrenia

BACKGROUND

Schizophrenia is a severe mental illness in which, despite the growing number of antipsychotics from 30 to 50% of patients remain resistant to treatment. Many resistance factors have been identified. Dissociation as a clinical phenomenon is associated with a loss of integrity between memories and perceptions of reality. Dissociative symptoms have also been found in patients with schizophrenia of varying severity. The established dispersion of the degree of dissociation in patients with schizophrenia gave us reason to look for the connection between the degree of dissociation and resistance to therapy.

METHODS

The type of study is correlation analysis. 106 patients with schizophrenia were evaluated. Of these, 45 with resistant schizophrenia and 60 with clinical remission. The Positive and Negative Syndrome Scale (PANSS) and Brief Psychiatric Rating Scale (BPRS) scales were used to assess clinical symptoms. The assessment of dissociative symptoms was made with the scale for dissociative experiences (DES). Statistical methods were used to analyze the differences in results between the two groups of patients.

RESULTS

Patients with resistant schizophrenia have a higher level of dissociation than patients in remission. This difference is significant and demonstrative with more than twice the level of dissociation in patients with resistant schizophrenia.The level of dissociation measured in patients with resistant schizophrenia is as high as the points on the DES in dissociative personality disorder.

CONCLUSION

Patients with resistant schizophrenia have a much higher level of dissociation than patients in clinical remission. The established difference between the two groups support to assume that resistance to the administered antipsychotics is associated with the presence of high dissociation in the group of resistant patients. These results give us explanation to think about therapeutic options outside the field of antipsychotic drugs as well as to consider different strategies earlier in the diagnostic process.

[Twelve years of research domain criteria in psychiatric research and practice: claim and reality]

The research domain criteria (RDoC) initiative of the National Institute of Mental Health (NIMH) was presented 12 years ago. The RDoC provides a matrix for the systematic, dimensional and domain-based study of mental disorders that is not based on established disease entities as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Diseases (ICD). The primary aim of RDoC is to understand the nature of mental health and illness in terms of different extents of dysfunction in psychological/biological systems with interconnected diagnoses. This selective review article aims to provide a comprehensive overview of RDoC-based studies that have contributed to a better conceptual organization of mental disorders. Numerous promising and methodologically sophisticated studies on RDoC were identified. The number of scientific studies increased over time, indicating that dimensional research is increasingly being pursued in psychiatry. In summary, the RDoC initiative has a considerable potential to more precisely define the complexity of pathomechanisms underlying mental disorders; however, major challenges (e.g. small and heterogeneous study samples, unclear biomarker definitions and lack of replication studies) remain to be overcome in the future. Furthermore, it is plausible that a diagnostic system of the future will integrate categorical and dimensional approaches to arrive at a stratification that can underpin a precision medical approach in psychiatry.

Translating Across Circuits and Genetics Toward Progress in Fear- and Anxiety-Related Disorders

(Reprinted with permission from Am J Psychiatry 2020; 177:214-222).

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

BACKGROUND

Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining.

OBJECTIVE

The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice.

METHODS

This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed.

RESULTS

A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform.

CONCLUSIONS

Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients' daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

How to construct neuroscience-informed psychiatric classification? Towards nomothetic networks psychiatry

Psychiatry remains in a permanent state of crisis, which fragmented psychiatry from the field of medicine. The crisis in psychiatry is evidenced by the many different competing approaches to psychiatric illness including psychodynamic, biological, molecular, pan-omics, precision, cognitive and phenomenological psychiatry, folk psychology, mind-brain dualism, descriptive psychopathology, and postpsychiatry. The current "gold standard" Diagnostic and Statistical Manual of Mental Disorders/International Classification of Diseases taxonomies of mood disorders and schizophrenia are unreliable and preclude to employ a deductive reasoning approach. Therefore, it is not surprising that mood disorders and schizophrenia research was unable to revise the conventional classifications and did not provide more adequate therapeutic approaches. The aim of this paper is to explain the new nomothetic network psychiatry (NNP) approach, which uses machine learning methods to build data-driven causal models of mental illness by assembling risk-resilience, adverse outcome pathways (AOP), cognitome, brainome, staging, symptomatome, and phenomenome latent scores in a causal model. The latter may be trained, tested and validated with Partial Least Squares analysis. This approach not only allows to compute pathway-phenotypes or biosignatures, but also to construct reliable and replicable nomothetic networks, which are, therefore, generalizable as disease models. After integrating the validated feature vectors into a well-fitting nomothetic network, clustering analysis may be applied on the latent variable scores of the R/R, AOP, cognitome, brainome, and phenome latent vectors. This pattern recognition method may expose new (transdiagnostic) classes of patients which if cross-validated in independent samples may constitute new (transdiagnostic) nosological categories.

The Value of Neuroimaging Techniques in the Translation and Transdiagnostic Validation of Psychiatric Diagnoses - Selective Review

Psychiatric diagnosis has long been perceived as more of an art than a science since its foundations lie within the observation, and the self-report of the patients themselves and objective diagnostic biomarkers are lacking. Furthermore, the diagnostic tools in use not only stray away from the conventional medical framework but also remain invalidated with evidence-based concepts. However, neuroscience, as a source of valid objective knowledge has initiated the process of a paradigm shift underlined by the main concept of psychiatric disorders being "brain disorders". It is also a bridge closing the explanatory gap among the different fields of medicine via the translation of the knowledge within a multidisciplinary framework. The contemporary neuroimaging methods, such as fMRI provide researchers with an entirely new set of tools to reform the current status quo by creating an opportunity to define and validate objective biomarkers that can be translated into clinical practice. Combining multiple neuroimaging techniques with the knowledge of the role of genetic factors, neurochemical imbalance and neuroinflammatory processes in the etiopathophysiology of psychiatric disorders is a step towards a comprehensive biological explanation of psychiatric disorders and a final differentiation of psychiatry as a well-founded medical science. In addition, the neuroscientific knowledge gained thus far suggests a necessity for directional change to exploring multidisciplinary concepts, such as multiple causality and dimensionality of psychiatric symptoms and disorders. A concomitant viewpoint transition of the notion of validity in psychiatry with a focus on an integrative validatory approach may facilitate the building of a collaborative bridge above the wall existing between the scientific fields analyzing the mind and those studying the brain.

Turning strains into strengths for understanding psychiatric disorders

There is a paucity in the development of new mechanistic insights and therapeutic approaches for treating psychiatric disease. One of the major challenges is reflected in the growing consensus that risk for these diseases is not determined by a single gene, but rather is polygenic, arising from the action and interaction of multiple genes. Canonically, experimental models in mice have been designed to ascertain the relative contribution of a single gene to a disease by systematic manipulation (e.g., mutation or deletion) of a known candidate gene. Because these studies have been largely carried out using inbred isogenic mouse strains, in which there is no (or very little) genetic diversity among subjects, it is difficult to identify unique allelic variants, gene modifiers, and epigenetic factors that strongly affect the nature and severity of these diseases. Here, we review various methods that take advantage of existing genetic diversity or that increase genetic variance in mouse models to (1) strengthen conclusions of single-gene function; (2) model diversity among human populations; and (3) dissect complex phenotypes that arise from the actions of multiple genes.

Translating Across Circuits and Genetics Toward Progress in Fear- and Anxiety-Related Disorders

Anxiety and fear-related disorders are common and disabling, and they significantly increase risk for suicide and other causes of morbidity and mortality. However, there is tremendous potential for translational neuroscience to advance our understanding of these disorders, leading to novel and powerful interventions and even to preventing their initial development. This overview examines the general circuits and processes thought to underlie fear and anxiety, along with the promise of translational research. It then examines some of the data-driven "next-generation" approaches that are needed for discovery and understanding but that do not always fit neatly into established models. From one perspective, these disorders offer among the most tractable problems in psychiatry, with a great deal of accumulated understanding, across species, of neurocircuit, behavioral, and, increasingly, genetic mechanisms, of how dysregulation of fear and threat processes contributes to anxiety-related disorders. One example is the progressively sophisticated understanding of how extinction underlies the exposure therapy component of cognitive-behavioral therapy approaches, which are ubiquitously used across anxiety and fear-related disorders. However, it is also critical to examine gaps in our understanding between reasonably well-replicated examples of successful translation, areas of significant deficits in knowledge, and the role of large-scale data-driven approaches in future progress and discovery. Although a tremendous amount of progress is still needed, translational approaches to understanding, treating, and even preventing anxiety and fear-related disorders offer great opportunities for successfully bridging neuroscience discovery to clinical practice.

Healthy Subjects With Extreme Patterns of Performance Differ in Functional Network Topology and Benefits From Nicotine

Do subjects with atypical patterns in attentional and executive behaviour show different brain network topology and react differently towards nicotine administration? The efficacy of pro-cognitive drugs like nicotine considerably varies between subjects and previous theoretical and empirical evidence suggest stronger behavioural nicotine effects in subjects with low performance. One problem is, however, how to best define low performance, especially if several cognitive functions are assessed for subject characterisation. We here present a method that used a multivariate, robust outlier detection algorithm to identify subjects with suspicious patterns of performance in attentional and executive functioning. In contrast to univariate approaches, this method is sensitive towards extreme positions within the multidimensional space that do not have to be extreme values in the individual behavioural distributions. The method was applied to a dataset of healthy, non-smoking subjects (n = 34) who were behaviorally characterised by an attention and executive function test on which N = 12 volunteers were classified as outliers. All subjects then underwent a resting-state functional magnetic resonance imaging (fMRI) scan to characterise brain network topology and an experimental behavioural paradigm under placebo and nicotine (7 mg patch) that gauged aspects of attention and executive function. Our results indicate that subjects with an atypical multivariate pattern in attention and executive functioning showed significant differences in nodal brain network integration in visual association and pre-motor brain regions during resting state. These differences in brain network topology significantly predicted larger individual nicotine effects on attentional processing. In summary, the current approach successfully identified a subgroup of healthy volunteers with low behavioural performance who differ in brain network topology and attentional benefit from nicotine.

Scientific Issues Relevant to Improving the Diagnosis, Risk Assessment, and Treatment of Major Depression

Over the past two decades, research in the biology and treatment of major depression has led to advances in our understanding of the biology of the disorder and to the development of novel treatments. While progress has been made, a number of key issues have emerged regarding diagnosis of the disorder and how we develop and test new therapies. Among these are the potential need to include new dimensions in the diagnostic criteria, the limited utility of clinical predictors of response, the moving away from traditional blinded trials in major depression, and whether preclinical models tell us much about novel drug development. These issues need to be addressed to avoid the field's embarking on trails of research and treatment development that could actually mislead or misdirect our efforts to develop better diagnostic tools and more effective treatments. Possible solutions to these problems are proposed.