Research Domain Criteria: Strengths, Weaknesses, and Potential Alternatives for Future Psychiatric Research

Synaptic changes in psychiatric and neurological disorders: state-of-the art of in vivo imaging

Synapses are implicated in many neuropsychiatric illnesses. Here, we provide an overview of in vivo techniques to index synaptic markers in patients. Several positron emission tomography (PET) tracers for synaptic vesicle glycoprotein 2 A (SV2A) show good reliability and selectivity. We review over 50 clinical studies including over 1700 participants, and compare findings in healthy ageing and across disorders, including addiction, schizophrenia, depression, posttraumatic stress disorder, and neurodegenerative disorders, including tauopathies, Huntington's disease and α-synucleinopathies. These show lower SV2A measures in cortical brain regions across most of these disorders relative to healthy volunteers, with the most well-replicated findings in tauopathies, whilst changes in Huntington's chorea, Parkinson's disease, corticobasal degeneration and progressive supranuclear palsy are predominantly subcortical. SV2A PET measures are correlated with functional connectivity across brain networks, and a number of other measures of brain function, including glucose metabolism. However, the majority of studies found no relationship between grey matter volume measured with magnetic resonance imaging and SV2A PET measures. Cognitive dysfunction, in domains including working memory and executive function, show replicated inverse relationships with SV2A measures across diagnoses, and initial findings also suggest transdiagnostic relationships with mood and anxiety symptoms. This suggests that synaptic abnormalities could be a common pathophysiological substrate underlying cognitive and, potentially, affective symptoms. We consider limitations of evidence and future directions; highlighting the need to develop postsynaptic imaging markers and for longitudinal studies to test causal mechanisms.

RDoC Framework Through the Lens of Predictive Processing: Focusing on Cognitive Systems Domain

In response to shortcomings of the current classification system in translating discoveries from basic science to clinical applications, NIMH offers a new framework for studying mental health disorders called Research Domain Criteria (RDoC). This framework holds a multidimensional outlook on psychopathologies focusing on functional domains of behavior and their implementing neural circuits. In parallel, the Predictive Processing (PP) framework stands as a leading theory of human brain function, offering a unified explanation for various types of information processing in the brain. While both frameworks share an interest in studying psychopathologies based on pathophysiology, their integration still needs to be explored. Here, we argued in favor of the explanatory power of PP to be a groundwork for the RDoC matrix in validating its constructs and creating testable hypotheses about mechanistic interactions between molecular biomarkers and clinical traits. Together, predictive processing may serve as a foundation for achieving the goals of the RDoC framework.

Association of RDoC dimensions with post-mortem brain transcriptional profiles in Alzheimer's Disease

INTRODUCTION

Neuropsychiatric symptoms are common in people with Alzheimer's disease (AD) across all severity stages. Their heterogeneous presentation and variable temporal association with cognitive decline suggest shared and distinct biological mechanisms. We hypothesized that specific patterns of gene expression associate with distinct NIMH Research Domain Criteria (RDoC) domains in AD.

METHODS

Post-mortem bulk RNAseq on the insula and anterior cingulate cortex from 60 brain donors representing the spectrum of canonical AD neuropathology combined with natural language processing approaches based on the RDoC Clinical Domains.

RESULTS

Distinct sets of >100 genes (p FDR <0.05) were specifically associated with at least one clinical domain (Cognitive, Social, Negative, Positive, Arousal). In addition, dysregulation of immune response pathways was shared across domains and brain regions.

DISCUSSION

Our findings provide evidence for distinct transcriptional profiles associated with RDoC domains suggesting that each dimension is characterized by specific sets of genes providing insight into the underlying mechanisms.

The absurdity of the latent disease model in mental health: 10,130,814 ways to have a DSM-5-TR psychological disorder

BACKGROUND

Latent disease classification is currently the accepted approach to mental illness diagnosis. In the United States, this takes the form of the Diagnostic and Statistical Manual of Mental Disorders-5-Text Revision (DSM-5-TR). Latent disease classification has been criticized for reliability and validity problems, particularly regarding diagnostic heterogeneity. No authors have calculated the scope of the heterogeneity problem of the entire DSM-5-TR.

AIMS

We addressed this issue by calculating the unique diagnostic profiles that exist for every DSM-5-TR diagnosis.

METHODS

We did this by applying formulas previously used in smaller heterogeneity analyses to all diagnoses within the DSM-5-TR.

RESULTS

We found that there are 10,130,814 ways to be diagnosed with a mental illness using DSM-5-TR criteria. When specifiers are considered, this number balloons to over 161 septillion unique diagnostic presentations (driven mainly by bipolar II disorder). Additionally, there are 1,951,065 ways to present with psychiatric symptoms, yet not meet diagnostic criteria.

CONCLUSIONS

Latent disease classification leads to considerable heterogeneity in possible presentations. We provide examples of how latent disease classification harms research and treatment programs. We echo recommendations for the dismissal of latent disease classification as a mental illness diagnostic program.

Mental health promotion and the positive concept of health: Navigating dilemmas

A prevailing view holds that the main goal of mental health promotion is to maintain and improve positive mental health, which is not merely defined by the absence of mental disorders, but by the presence of certain abilities. There are, however, challenges associated with this view that this paper aims to identify and explore. We start by highlighting three requirements for an ethically and politically justified mental health promotion scheme: (i) using a positive concept of mental health that (ii) respects the neutrality principle while (iii) not being overly permissive. Then, we argue that the WHO's positive concept of health violates (ii), and continue by exploring three philosophical accounts (i.e., Nordenfelt, 1995, 2017; Graham 2010; Wren-Lewis & Alexandrova, 2021) that could potentially provide a solution. We show that these face a dilemma of their own: they either violate (ii) or (iii), and they can rectify one issue only by violating the other. Considering the problems linked to the positive notion of health, the final section explores the alternate route of rejecting proposition (i) and instead embracing a negative concept of health. We argue that this option does not present a more advantageous solution. We conclude by highlighting the necessity for additional research to tackle the challenges we identified.

Do We Need a Novel Framework for Classifying Psychopathology? A Discussion Paper

Introduction

The ICD-11 and DSM-5 are the leading systems for the classification of mental disorders, and their relevance for clinical work and research, as well as their impact for policy making and legal questions, has increased considerably. In recent years, other frameworks have been proposed to supplement or even replace the ICD and the DSM, raising many questions regarding clinical utility, scientific relevance, and, at the core, how best to conceptualize mental disorders.

Method

As examples of the new approaches that have emerged, here we introduce the Hierarchical Taxonomy of Psychopathology (HiTOP), the Research Domain Criteria (RDoC), systems and network approaches, process-based approaches, as well as a new approach to the classification of personality disorders.

Results and Discussion

We highlight main distinctions between these classification frameworks, largely related to different priorities and goals, and discuss areas of overlap and potential compatibility. Synergies among these systems may provide promising new avenues for research and clinical practice.

Major depressive disorder

Major depressive disorder (MDD) is characterized by persistent depressed mood, loss of interest or pleasure in previously enjoyable activities, recurrent thoughts of death, and physical and cognitive symptoms. People with MDD can have reduced quality of life owing to the disorder itself as well as related medical comorbidities, social factors, and impaired functional outcomes. MDD is a complex disorder that cannot be fully explained by any one single established biological or environmental pathway. Instead, MDD seems to be caused by a combination of genetic, environmental, psychological and biological factors. Treatment for MDD commonly involves pharmacological therapy with antidepressant medications, psychotherapy or a combination of both. In people with severe and/or treatment-resistant MDD, other biological therapies, such as electroconvulsive therapy, may also be offered.

Dimensional clinical phenotyping using post-mortem brain donor medical records: post-mortem RDoC profiling is associated with Alzheimer's disease neuropathology

Introduction

Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to post-mortem work, as assessments of phenotypic concepts need to rely on existing records.

Methods

We adapted well-validated methodologies to compute National Institute of Mental Health Research Domain Criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether cognitive domain scores were associated with Alzheimer's disease neuropathological measures.

Results

Our results confirm an association of EHR-derived cognitive scores with neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß = 0.38, P = 0.0004), parietal (ß = 0.35, P = 0.0008), temporal (ß = 0.37, P = 0.0004) and occipital (ß = 0.37, P = 0.0003) lobes.

Discussion

This proof-of-concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from post-mortem EHR. The associations may accelerate post-mortem brain research beyond classical case-control designs.

Exploring the latent structure of behavior using the Human Connectome Project's data

How behavior arises from brain physiology has been one central topic of investigation in neuroscience. Considering the recent interest in predicting behavior from brain imaging using open datasets, there is the need for a principled approach to the categorization of behavioral variables. However, this is not trivial, as the definitions of psychological constructs and their relationships-their ontology-are not always clear. Here, we propose to use exploratory factor analysis (EFA) as a data-driven approach to find robust and interpretable domains of behavior in the Human Connectome Project (HCP) dataset. Additionally, we explore the clustering of behavioral variables using consensus clustering. We find that four and five factors offer the best description of the data, a result corroborated by the consensus clustering. In the four-factor solution, factors for Mental Health, Cognition, Processing Speed, and Substance Use arise. With five factors, Mental Health splits into Well-Being and Internalizing. Clustering results show a similar pattern, with clusters for Cognition, Processing Speed, Positive Affect, Negative Affect, and Substance Use. The factor structure is replicated in an independent dataset using confirmatory factor analysis (CFA). We discuss how the content of the factors fits with previous conceptualizations of general behavioral domains.

Introducing a depression-like syndrome for translational neuropsychiatry: a plea for taxonomical validity and improved comparability between humans and mice

Depressive disorders are the most burdensome psychiatric disorders worldwide. Although huge efforts have been made to advance treatment, outcomes remain unsatisfactory. Many factors contribute to this gridlock including suboptimal animal models. Especially limited study comparability and replicability due to imprecise terminology concerning depressive-like states are major problems. To overcome these issues, new approaches are needed. Here, we introduce a taxonomical concept for modelling depression in laboratory mice, which we call depression-like syndrome (DLS). It hinges on growing evidence suggesting that mice possess advanced socioemotional abilities and can display non-random symptom patterns indicative of an evolutionary conserved disorder-like phenotype. The DLS approach uses a combined heuristic method based on clinical depression criteria and the Research Domain Criteria to provide a biobehavioural reference syndrome for preclinical rodent models of depression. The DLS criteria are based on available, species-specific evidence and are as follows: (I) minimum duration of phenotype, (II) significant sociofunctional impairment, (III) core biological features, (IV) necessary depressive-like symptoms. To assess DLS presence and severity, we have designed an algorithm to ensure statistical and biological relevance of findings. The algorithm uses a minimum combined threshold for statistical significance and effect size (p value ≤ 0.05 plus moderate effect size) for each DLS criterion. Taken together, the DLS is a novel, biologically founded, and species-specific minimum threshold approach. Its long-term objective is to gradually develop into an inter-model validation standard and microframework to improve phenotyping methodology in translational research.

Epilepsy and its neurobehavioral comorbidities: Insights gained from animal models

It is well established that epilepsy is associated with numerous neurobehavioral comorbidities, with a bidirectional relationship; people with epilepsy have an increased incidence of depression, anxiety, learning and memory difficulties, and numerous other psychosocial challenges, and the occurrence of epilepsy is higher in individuals with those comorbidities. Although the cause-and-effect relationship is uncertain, a fuller understanding of the mechanisms of comorbidities within the epilepsies could lead to improved therapeutics. Here, we review recent data on epilepsy and its neurobehavioral comorbidities, discussing mainly rodent models, which have been studied most extensively, and emphasize that clinically relevant information can be gained from preclinical models. Furthermore, we explore the numerous potential factors that may confound the interpretation of emerging data from animal models, such as the specific seizure induction method (e.g., chemical, electrical, traumatic, genetic), the role of species and strain, environmental factors (e.g., laboratory environment, handling, epigenetics), and the behavioral assays that are chosen to evaluate the various aspects of neural behavior and cognition. Overall, the interplay between epilepsy and its neurobehavioral comorbidities is undoubtedly multifactorial, involving brain structural changes, network-level differences, molecular signaling abnormalities, and other factors. Animal models are well poised to help dissect the shared pathophysiological mechanisms, neurological sequelae, and biomarkers of epilepsy and its comorbidities.

Clinical Utility of Fluid Biomarker in Depressive Disorder

Major depressive disorders are ranked as the single largest contributor to non-fatal health loss and biomarkers could largely improve our routine clinical activity by predicting disease course and guiding treatment. However there is still a dearth of valid biomarkers in the field of psychiatry. The initial assumption that a single biomarker can capture the myriad of complex processes proved to be naive. The purpose of this paper is to critically review the field and to illustrate the possible practical application for routine clinical care. Biomarkers derived from DNA analysis are the ones that have received the most attention. Other potential candidates include circulating transcription products, proteins, and inflammatory markers. DNA polygenic risk scores proved to be useful in other fields of medicine and preliminary results suggest that they could be useful both as risk and diagnostic biomarkers also in depression and for the choice of treatment. A number of other possible fluid biomarkers are currently under investigation for diagnosis, outcome prediction, staging, and stratification of interventions, however research is still needed before they can be used for routine clinical care. When available, clinicians may be able to receive a lab report with detailed information about disease risk, outcome prediction, and specific indications about preferred treatments.

Functional Connectome-Based Predictive Modeling in Autism

Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.

Another failure of the latent disease model? The case of compulsive sexual behavior disorder •

Recent debates have evolved regarding the classification/conceptualization of compulsive sexual behavior disorder (CSBD). Conclusions regarding an agreed upon CSBD model are hindered by reliance on the latent disease model. Competing biological-based frameworks are moving forward to replace latent disease classification more broadly but have been met with limited success. We suggest that CSBD researchers move towards developing dimensional, transtheoretical, process-based models. We further suggest additional research, particularly mixed methods and longitudinal studies. Finally, we request that federal funding bodies take a more active role in supporting CSBD research.

Schizophrenia: A Narrative Review of Etiopathogenetic, Diagnostic and Treatment Aspects

Although schizophrenia is currently conceptualized as being characterized as a syndrome that includes a collection of signs and symptoms, there is strong evidence of heterogeneous and complex underpinned etiological, etiopathogenetic, and psychopathological mechanisms, which are still under investigation. Therefore, the present viewpoint review is aimed at providing some insights into the recently investigated schizophrenia research fields in order to discuss the potential future research directions in schizophrenia research. The traditional schizophrenia construct and diagnosis were progressively revised and revisited, based on the recently emerging neurobiological, genetic, and epidemiological research. Moreover, innovative diagnostic and therapeutic approaches are pointed to build a new construct, allowing the development of better clinical and treatment outcomes and characterization for schizophrenic individuals, considering a more patient-centered, personalized, and tailored-based dimensional approach. Further translational studies are needed in order to integrate neurobiological, genetic, and environmental studies into clinical practice and to help clinicians and researchers to understand how to redesign a new schizophrenia construct.

Using animal models for the studies of schizophrenia and depression: The value of translational models for treatment and prevention

Animal models of psychiatric disorders have been highly effective in advancing the field, identifying circuits related to pathophysiology, and identifying novel therapeutic targets. In this review, we show how animal models, particularly those based on development, have provided essential information regarding circuits involved in disorders, disease progression, and novel targets for intervention and potentially prevention. Nonetheless, in recent years there has been a pushback, largely driven by the US National Institute of Mental Health (NIMH), to shift away from animal models and instead focus on circuits in normal subjects. This has been driven primarily from a lack of discovery of new effective therapeutic targets, and the failure of targets based on preclinical research to show efficacy. We discuss why animal models of complex disorders, when strongly cross-validated by clinical research, are essential to understand disease etiology as well as pathophysiology, and direct new drug discovery. Issues related to shortcomings in clinical trial design that confound translation from animal models as well as the failure to take patient pharmacological history into account are proposed to be a source of the failure of what are likely effective compounds from showing promise in clinical trials.

A longitudinal resource for studying connectome development and its psychiatric associations during childhood

Most psychiatric disorders are chronic, associated with high levels of disability and distress, and present during pediatric development. Scientific innovation increasingly allows researchers to probe brain-behavior relationships in the developing human. As a result, ambitions to (1) establish normative pediatric brain development trajectories akin to growth curves, (2) characterize reliable metrics for distinguishing illness, and (3) develop clinically useful tools to assist in the diagnosis and management of mental health and learning disorders have gained significant momentum. To this end, the NKI-Rockland Sample initiative was created to probe lifespan development as a large-scale multimodal dataset. The NKI-Rockland Sample Longitudinal Discovery of Brain Development Trajectories substudy (N = 369) is a 24- to 30-month multi-cohort longitudinal pediatric investigation (ages 6.0-17.0 at enrollment) carried out in a community-ascertained sample. Data include psychiatric diagnostic, medical, behavioral, and cognitive phenotyping, as well as multimodal brain imaging (resting fMRI, diffusion MRI, morphometric MRI, arterial spin labeling), genetics, and actigraphy. Herein, we present the rationale, design, and implementation of the Longitudinal Discovery of Brain Development Trajectories protocol.

Early deviant behaviour as a dimension trait and endophenotype in schizophrenia

Background

In psychiatry, there is still a lack of objective biological diagnostic measurements. It is important to investigate measurements or symptom dimensions that can inform diagnostic assessments and allow for a more personalised approach to patients.

Aim

To discuss how early deviant behaviour (EDB) may be seen as a possible continuous symptom dimension trait and endophenotype in schizophrenia.

Methods

Conducting a commentary review by highlighting some important findings from available literature.

Results

Findings regarding EDB in schizophrenia in a South African genetic sample point towards EDB as a progressive subtype of schizophrenia, with very early onset of illness (even prior to the psychotic symptomatology) and a genetic form of illness.

Conclusion

Valuable information can be gained by enquiring into EDB and viewing it as a continuous symptom dimension trait and endophenotype during the psychiatric diagnostic interview.

Psychedelic Therapy's Transdiagnostic Effects: A Research Domain Criteria (RDoC) Perspective

Accumulating clinical evidence shows that psychedelic therapy, by synergistically combining psychopharmacology and psychological support, offers a promising transdiagnostic treatment strategy for a range of disorders with restricted and/or maladaptive habitual patterns of emotion, cognition and behavior, notably, depression (MDD), treatment resistant depression (TRD) and addiction disorders, but perhaps also anxiety disorders, obsessive-compulsive disorder (OCD), Post-Traumatic Stress Disorder (PTSD) and eating disorders. Despite the emergent transdiagnostic evidence, the specific clinical dimensions that psychedelics are efficacious for, and associated underlying neurobiological pathways, remain to be well-characterized. To this end, this review focuses on pre-clinical and clinical evidence of the acute and sustained therapeutic potential of psychedelic therapy in the context of a transdiagnostic dimensional systems framework. Focusing on the Research Domain Criteria (RDoC) as a template, we will describe the multimodal mechanisms underlying the transdiagnostic therapeutic effects of psychedelic therapy, traversing molecular, cellular and network levels. These levels will be mapped to the RDoC constructs of negative and positive valence systems, arousal regulation, social processing, cognitive and sensorimotor systems. In summarizing this literature and framing it transdiagnostically, we hope we can assist the field in moving toward a mechanistic understanding of how psychedelics work for patients and eventually toward a precise-personalized psychedelic therapy paradigm.

[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.

Experimental models in psychopathology research: The relation between Research Domain Criteria and Experimental Psychopathology

Experimental Psychopathology (EPP) and the Research Domain Criteria (RDoC) are research approaches that have developed in parallel, providing inter-related yet different scientific frameworks to investigate psychopathology at the intersection of fundamental and applied research. Here we address the overlap and differences between RDoC and EPP, and the challenges that both approaches face. Although overlap between EPP and RDoC can be clearly observed, each approach has its own unique strengths and weaknesses. These aspects will be illustrated by examples with respect to fear conditioning, an experimental procedure that has played a central role in both EPP and RDoC. We see much potential in boosting psychopathology research by combining the strengths of these two approaches.

Hearing Voices and Seeing Things: Symptoms of Anxiety Misconstrued as Evidence of Schizophrenia in an Adolescent

A patient's complaint of "hearing voices" or "seeing things" or of similar perceptual abnormalities leaves the clinician with 2 decisions: (1) Is the patient actually experiencing a hallucination, or does the complaint reflect a different mental experience, ranging from outright fabrication to the misinterpretation or mislabeling of vivid thoughts and emotions? (2) How should the experience reported by the patient, whether determined to be a hallucination or not, be understood in the context of the patient's entire history and mental state? We report the case of a 16-year-old whose cartoon-like hallucinations had led to the diagnosis of schizophrenia and had directed attention of the patient, her parents, and her clinicians away from critical issues of anxiety, depression, learning difficulties, and traumatic school experiences. This case illustrates how the diagnosis of schizophrenia can be driven by the prominence and vividness of psychotic-like symptoms reported by a patient, the expectation that patients' chief complaints must be directly and immediately addressed, insufficient attention to collateral information, and the distortions of a "checklist" approach to psychiatric diagnosis driven by the criteria in the Diagnostic and Statistical Manual of Mental Disorders, insurers, and the properties of electronic medical records. Given the consequences of either underdiagnosing or overdiagnosing schizophrenia, and the current lack of validated objective tests to assist with this diagnosis, clinicians are obligated to perform a thorough clinical assessment of such patients, including a probing exploration of the patient's mental state and a systematic collection of collateral information.

Psychiatric Neural Networks and Precision Therapeutics by Machine Learning

Learning and environmental adaptation increase the likelihood of survival and improve the quality of life. However, it is often difficult to judge optimal behaviors in real life due to highly complex social dynamics and environment. Consequentially, many different brain regions and neuronal circuits are involved in decision-making. Many neurobiological studies on decision-making show that behaviors are chosen through coordination among multiple neural network systems, each implementing a distinct set of computational algorithms. Although these processes are commonly abnormal in neurological and psychiatric disorders, the underlying causes remain incompletely elucidated. Machine learning approaches with multidimensional data sets have the potential to not only pathologically redefine mental illnesses but also better improve therapeutic outcomes than DSM/ICD diagnoses. Furthermore, measurable endophenotypes could allow for early disease detection, prognosis, and optimal treatment regime for individuals. In this review, decision-making in real life and psychiatric disorders and the applications of machine learning in brain imaging studies on psychiatric disorders are summarized, and considerations for the future clinical translation are outlined. This review also aims to introduce clinicians, scientists, and engineers to the opportunities and challenges in bringing artificial intelligence into psychiatric practice.

What a finding of gene copy number variation can add to the diagnosis of developmental neuropsychiatric disorders

Among medical disciplines, diagnosis in psychiatry depends highly upon descriptive signs and symptoms, rather than biomarkers. Clear descriptions of specific genetic etiologies have been lacking; genomic technologies, however, are rapidly changing that landscape. Notably, chromosomal microarrays-which detect gene copy number variants (CNVs)-are a recommended standard of care for neurodevelopmental disorders. As a result, an increasing number of patients now receive a clinical diagnosis based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and an identified genetic etiological variant. However, psychiatric and genetic diagnoses are frequently communicated and managed as two disconnected diagnostic parameters. Here, we advocate for a transition model, allowing the integration of genetic etiological information-starting with diagnostically proven CNVs-within the DSM-5 classification framework.

STAT3 in the dorsal raphe gates behavioural reactivity and regulates gene networks associated with psychopathology

The signal transducer and activator of transcription 3 (STAT3) signalling pathway is activated through phosphorylation by Janus kinases in response to a diverse set of immunogenic and non-immunogenic triggers. Several distinct lines of evidence propose an intricate involvement of STAT3 in neural function relevant to behaviour in health and disease. However, in part due to the pleiotropic effects resulting from its DNA binding activity and the consequent regulation of expression of a variety of genes with context-dependent cellular consequences, the precise nature of STAT3 involvement in the neural mechanisms underlying psychopathology remains incompletely understood. Here, we focused on the midbrain serotonergic system, a central hub for the regulation of emotions, to examine the relevance of STAT3 signalling for emotional behaviour in mice by selectively knocking down raphe STAT3 expression using germline genetic (STAT3 KO) and viral-mediated approaches. Mice lacking serotonergic STAT3 presented with reduced negative behavioural reactivity and a blunted response to the sensitising effects of amphetamine, alongside alterations in midbrain neuronal firing activity of serotonergic neurons and transcriptional control of gene networks relevant for neuropsychiatric disorders. Viral knockdown of dorsal raphe (DR) STAT3 phenocopied the behavioural alterations of STAT3 KO mice, excluding a developmentally determined effect and suggesting that disruption of STAT3 signalling in the DR of adult mice is sufficient for the manifestation of behavioural traits relevant to psychopathology. Collectively, these results suggest DR STAT3 as a molecular gate for the control of behavioural reactivity, constituting a mechanistic link between the upstream activators of STAT3, serotonergic neurotransmission and psychopathology.

Comorbidity in Schizophrenia: Conceptual Issues and Clinical Management

Schizophrenia is a complex psychiatric disorder that affects cognitive, perceptual, and emotional functioning. The currently available evidence suggests heterogenous intertwining of biological and psychosocial etio-pathogeneses. Clinical and research interests in the comorbidity issues of schizophrenia were borne out of the real-world clinical challenges that patients often present with multiple coexisting psychopathologies as well as comorbid medical conditions. The recent DSM-5 shift toward a symptom dimensional-based perspective, the NIMH Research Domain Criteria (RDoC) initiative to examine biopsychosocial pathogeneses in mental illness, and the FDA's emphasis on real world-based clinical trial criterion all have promoted a shift in clinical research that has facilitated understanding and treatment of comorbidity in schizophrenia. This emerging conceptual shift as well as pharmacological developments that address the multidimensional pathogeneses in schizophrenia may pave the way for a better understanding and treatment.

Muscle dysmorphia: A systematic and meta-analytic review of the literature to assess diagnostic validity

OBJECTIVE

Although muscle dysmorphia (MD) is a new addition to DSM-5 as a specifier of body dysmorphic disorder (BDD), previous studies have treated MD as a stand-alone diagnosis. We aimed to assess the validity of MD as a stand-alone diagnosis via systematic and meta-analytic review of MD literature using both Robins and Guze criteria and additional criteria from Kendler.

METHOD

We performed a systematic search of ProQuest, PsycInfo, and PubMed databases for the period of January 1993 to October 2019 resulting in 40 papers to examine Robins and Guze's criteria (clinical picture) as well as those added by Kendler (antecedent validators; concurrent validators; predictive validators).

RESULTS

We identified two distinct symptomatic presentations of MD using cluster analysis, a behavioral type and cognitive/behavioral type. For examining the concurrent validators, quantitative meta-analyses differentiated MD populations from controls; however, results were inconclusive in delineating MD from existing disorders. For assessing antecedent and predictive validators, the symptomatic profiles, treatment response, and familial links for MD were similar to those for BDD and for eating disorders.

DISCUSSION

We found preliminary support for MD as a clinically valid presentation, but insufficient evidence to determine whether it is best categorized as a specifier of BDD or unique psychiatric condition.

The Subgrouping Structure of Newborns with Heterogenous Brain-Behavior Relationships

The presence of heterogeneity/subgroups in infants and older populations against single-domain brain or behavioral measures has been previously characterized. However, few attempts have been made to explore heterogeneity at the brain-behavior relationship level. Such a hypothesis posits that different subgroups of infants may possess qualitatively different brain-behavior relationships that could ultimately contribute to divergent developmental outcomes even with relatively similar brain phenotypes. In this study, we aimed to explore such relationship-level heterogeneity and delineate the subgrouping structure of newborns with differential brain-behavior associations based on a typically developing sample of 81 infants with 3-week resting-state functional magnetic resonance imaging scans and 4-year intelligence quotient (IQ) measures. Our results not only confirmed the existence of relationship-level heterogeneity in newborns but also revealed divergent developmental outcomes associated with two subgroups showing similar brain functional connectivity but contrasting brain-behavior relationships. Importantly, further analyses unveiled an intriguing pattern that the subgroup with higher 4-year IQ outcomes possessed brain-behavior relationships that were congruent to their functional connectivity pattern in neonates while the subgroup with lower 4-year IQ not, providing potential explanations for the observed IQ differences. The characterization of heterogeneity at the brain-behavior relationship level may not only improve our understanding of the patterned intersubject variability during infancy but could also pave the way for future development of heterogeneity-inspired, personalized, subgroup-specific models for better prediction.

Combining local and global evolutionary trajectories of brain-behaviour relationships through game theory

The study of the evolution of brain-behaviour relationships concerns understanding the causes and repercussions of cross- and within-species variability. Understanding such variability is a main objective of evolutionary and cognitive neuroscience, and it may help explaining the appearance of psychopathological phenotypes. Although brain evolution is related to the progressive action of selection and adaptation through multiple paths (e.g. mosaic vs. concerted evolution, metabolic vs. structural and functional constraints), a coherent, integrative framework is needed to combine evolutionary paths and neuroscientific evidence. Here, we review the literature on evolutionary pressures focusing on structural-functional changes and developmental constraints. Taking advantage of recent progress in neuroimaging and cognitive neuroscience, we propose a twofold hypothetical model of brain evolution. Within this model, global and local trajectories imply rearrangements of neural subunits and subsystems and of behavioural repertoires of a species, respectively. We incorporate these two processes in a game in which the global trajectory shapes the structural-functional neural substrates (i.e. players), while the local trajectory shapes the behavioural repertoires (i.e. stochastic payoffs).

α7 nicotinic acetylcholine receptors as therapeutic targets in schizophrenia: Update on animal and clinical studies and strategies for the future

Schizophrenia is a devastating mental illness and its effective treatment is among the most challenging issues in psychiatry. The symptoms of schizophrenia are heterogeneous ranging from positive symptoms (e.g., delusions, hallucinations) to negative symptoms (e.g., anhedonia, social withdrawal) to cognitive dysfunction. Antipsychotics are effective at ameliorating positive symptoms in some patients; however, they are not reliably effective at improving the negative symptoms or cognitive impairments. The inability to address the cognitive impairments is a particular concern since they have the greatest long-term impact on functional outcomes. While decades of research have been devoted to the development of pro-cognitive agents for schizophrenia, to date, no drug has been approved for clinical use. Converging behavioral, neurobiological, and genetic evidence led to the identification of the α7-nicotinic acetylcholine receptor (α7-nAChR) as a therapeutic target several years ago and there is now extensive preclinical evidence that α7-nAChR ligands have pro-cognitive effects and other properties that should be beneficial to schizophrenia patients. However, like the other pro-cognitive strategies, no α7-nAChR ligand has been approved for clinical use in schizophrenia thus far. In this review, several topics are discussed that may impact the success of α7-nAChR ligands as pro-cognitive agents for schizophrenia including the translational value of the animal models used, clinical trial design limitations, confounding effects of polypharmacy, dose-effect relationships, and chronic versus intermittent dosing considerations. Determining the most optimal pharmacologic strategy at α7-nAChRs: agonist, positive allosteric modulator, or potentially even receptor antagonist is also discussed. article is part of the special issue on 'Contemporary Advances in Nicotine Neuropharmacology'.