Transdiagnostic psychiatry: a systematic review

The usefulness of current psychiatric classification, which is based on ICD/DSM categorical diagnoses, remains questionable. A promising alternative has been put forward as the “transdiagnostic” approach. This is expected to cut across existing categorical diagnoses and go beyond them, to improve the way we classify and treat mental disorders. This systematic review explores whether self‐defining transdiagnostic research meets such high expectations. A multi‐step Web of Science literature search was performed according to an a priori protocol, to identify all studies that used the word “transdiagnostic” in their title, up to May 5, 2018. Empirical variables which indexed core characteristics were extracted, complemented by a bibliometric and conceptual analysis. A total of 111 studies were included. Most studies were investigating interventions, followed by cognition and psychological processes, and neuroscientific topics. Their samples ranged from 15 to 91,199 (median 148) participants, with a mean age from 10 to more than 60 (median 33) years. There were several methodological inconsistencies relating to the definition of the gold standard (DSM/ICD diagnoses), of the outcome measures and of the transdiagnostic approach. The quality of the studies was generally low and only a few findings were externally replicated. The majority of studies tested transdiagnostic features cutting across different diagnoses, and only a few tested new classification systems beyond the existing diagnoses. About one fifth of the studies were not transdiagnostic at all, because they investigated symptoms and not disorders, a single disorder, or because there was no diagnostic information. The bibliometric analysis revealed that transdiagnostic research largely restricted its focus to anxiety and depressive disorders. The conceptual analysis showed that transdiagnostic research is grounded more on rediscoveries than on true innovations, and that it is affected by some conceptual biases. To date, transdiagnostic approaches have not delivered a credible paradigm shift that can impact classification and clinical care. Practical “TRANSD”iagnostic recommendations are proposed here to guide future research in this field.     

Variability in the Efficacy of Psychopharmaceuticals: Contributions from Pharmacogenomics, Ethnopsychopharmacology, and Psychological and Psychiatric Anthropologies

Psychological and psychiatric anthropology have long questioned the universality of psychiatric diagnoses, bringing to light the fluidity of mental disorder, and recognizing that the experience and expression of psychopathology is influenced by complex and interacting genetic, environmental, and cultural factors. The majority of our discussions, however, have remained centered around the role of culture in shaping mental illness: drawing attention to subjective experiences of mental illness and culturally patterned modes of symptom presentation, and interrogating the cogency of universal diagnostic rubrics. Psychological and psychiatric anthropology have yet to robustly engage the broadly assumed universal validity of psychiatric medications and the ways in which they are prescribed and experienced. This article provides an introduction into the fields of pharmacogenomics and ethnopsychopharmacology, areas of inquiry seeking to understand the ways in which genetic variability occurring between, and within, large population groups influences individual ability to metabolize psychotropic medications. This piece further addresses the complex issue of psychopharmaceutical efficacy, stressing the ways in which, just as with psychopathology, medications and their outcomes are likewise influenced by the complex interactions of genes, environment, and culture. Lastly, ways in which anthropology can and should engage with the growing fields of pharmacogenomics and ethnopsychopharmacology are suggested.     

A biological meaningful classification in child psychiatry that is based upon ethological methods

An ethological approach to behavior quantification and classification in child psychiatry is described using the prototype approach to concept formation, in which examples are considered exemplars of concepts. Data are based on a large number of psychiatric evaluations. A number of behaviors was selected and their occurrence recorded and quantified. This resulted in a number of parameters for which values were determined per child. Classification of parameters was based on a number of explicit criteria that varied in their usefulness. It appeared that a classification on the basis of biological function was most useful. This biologically meaningful classification reasonably paralleled traditional child psychiatric diagnoses. The resulting prototypes may be useful during psychiatric evaluation of children.     

Optimality in mono- and multisensory map formation

In the struggle for survival in a complex and dynamic environment, nature has developed a multitude of sophisticated sensory systems. In order to exploit the information provided by these sensory systems, higher vertebrates reconstruct the spatio-temporal environment from each of the sensory systems they have at their disposal. That is, for each modality the animal computes a neuronal representation of the outside world, a monosensory neuronal map. Here we present a universal framework that allows to calculate the specific layout of the involved neuronal network by means of a general mathematical principle, viz., stochastic optimality. In order to illustrate the use of this theoretical framework, we provide a step-by-step tutorial of how to apply our model. In so doing, we present a spatial and a temporal example of optimal stimulus reconstruction which underline the advantages of our approach. That is, given a known physical signal transmission and rudimental knowledge of the detection process, our approach allows to estimate the possible performance and to predict neuronal properties of biological sensory systems. Finally, information from different sensory modalities has to be integrated so as to gain a unified perception of reality for further processing, e.g., for distinct motor commands. We briefly discuss concepts of multimodal interaction and how a multimodal space can evolve by alignment of monosensory maps.     

Progress in achieving quantitative classification of psychopathology

Shortcomings of approaches to classifying psychopathology based on expert consensus have given rise to contemporary efforts to classify psychopathology quantitatively. In this paper, we review progress in achieving a quantitative and empirical classification of psychopathology. A substantial empirical literature indicates that psychopathology is generally more dimensional than categorical. When the discreteness versus continuity of psychopathology is treated as a research question, as opposed to being decided as a matter of tradition, the evidence clearly supports the hypothesis of continuity. In addition, a related body of literature shows how psychopathology dimensions can be arranged in a hierarchy, ranging from very broad “spectrum level” dimensions, to specific and narrow clusters of symptoms. In this way, a quantitative approach solves the “problem of comorbidity” by explicitly modeling patterns of co‐occurrence among signs and symptoms within a detailed and variegated hierarchy of dimensional concepts with direct clinical utility. Indeed, extensive evidence pertaining to the dimensional and hierarchical structure of psychopathology has led to the formation of the Hierarchical Taxonomy of Psychopathology (HiTOP) Consortium. This is a group of 70 investigators working together to study empirical classification of psychopathology. In this paper, we describe the aims and current foci of the HiTOP Consortium. These aims pertain to continued research on the empirical organization of psychopathology; the connection between personality and psychopathology; the utility of empirically based psychopathology constructs in both research and the clinic; and the development of novel and comprehensive models and corresponding assessment instruments for psychopathology constructs derived from an empirical approach.     

Why genetic investigation of psychiatric disorders is so difficult

Genetic investigations of psychiatric disease have historically relied on subjectively assessed disease diagnoses to define phenotypes. Recent developments in several areas have provided various new approaches to behavioral disorder phenotyping that promise to advance our understanding of the genetic and environmental etiologies of these traits. Such developments include re-evaluation of the boundaries between different psychiatric categories, implementation of quantitative neurobiological assessments that may serve as endophenotypes, generation of increasingly sophisticated animal behavioral models, and investigation of explicit environmental covariates. At the same time, movement toward large-scale, collaborative efforts is increasing the effectiveness of traditional genetic mapping approaches.     

Association analysis of candidate genes for neuropsychiatric disease: the perpetual campaign

Association studies have been proposed to identify the genetic determinants of complex neuropsychiatric traits. Although such studies of candidate genes offer great potential to identify genetic variants that contribute to the expression of psychiatric disease, no consistent associations have been identified. Studies to date have focused on candidate genes that are selected for analysis on the basis of incomplete information about gene function in the brain, therefore the majority of genes expressed in the brain have been ignored. Additionally, most genetic determinants of psychiatric disease will probably be of modest effect and therefore require association studies of large samples. As genomic technologies advance, massive genotyping of large samples should allow identification of alleles that contribute to psychopathology.     

Applications of a formal approach to decipher discrete genetic networks

Background  

A growing demand for tools to assist the building and analysis of biological networks exists in systems biology. We argue that the use of a formal approach is relevant and applicable to address questions raised by biologists about such networks. The behaviour of these systems being complex, it is essential to exploit efficiently every bit of experimental information. In our approach, both the evolution rules and the partial knowledge about the structure and the behaviour of the network are formalized using a common constraint-based language.     

Life history strategies and psychopathology: the faster the life strategies,the more symptoms of psychopathology

There is little extant empirical literature examining the associations between life history strategies and symptoms of psychopathology. The current study (N = 138) investigated the associations between life history strategies, symptoms of psychopathology, aggression, incidence of self-harm behaviour, and attachment (perceived parental support) in sample drawn from the general population and community mental health service providers. The results from the study indicate those with a faster life strategy report greater levels of aggression and symptoms of psychopathology. Further, perceptions of poorer parental support were associated with a faster life history strategy. Implications for life history theory, conceptualising psychopathology, and future research directions are discussed.     

Associating Genes and Protein Complexes with Disease via Network Propagation

A fundamental challenge in human health is the identification of disease-causing genes. Recently, several studies have tackled this challenge via a network-based approach, motivated by the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process and are restricted to inferring single gene associations. Here, we provide a global, network-based method for prioritizing disease genes and inferring protein complex associations, which we call PRINCE. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. We exploit this function to predict not only genes but also protein complex associations with a disease of interest. We test our method on gene-disease association data, evaluating both the prioritization achieved and the protein complexes inferred. We show that our method outperforms extant approaches in both tasks. Using data on 1,369 diseases from the OMIM knowledgebase, our method is able (in a cross validation setting) to rank the true causal gene first for 34% of the diseases, and infer 139 disease-related complexes that are highly coherent in terms of the function, expression and conservation of their member proteins. Importantly, we apply our method to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus. PRINCE''s predictions for these diseases highly match the known literature, suggesting several novel causal genes and protein complexes for further investigation.     

A network theory of mental disorders

In recent years, the network approach to psychopathology has been advanced as an alternative way of conceptualizing mental disorders. In this approach, mental disorders arise from direct interactions between symptoms. Although the network approach has led to many novel methodologies and substantive applications, it has not yet been fully articulated as a scientific theory of mental disorders. The present paper aims to develop such a theory, by postulating a limited set of theoretical principles regarding the structure and dynamics of symptom networks. At the heart of the theory lies the notion that symptoms of psychopathology are causally connected through myriads of biological, psychological and societal mechanisms. If these causal relations are sufficiently strong, symptoms can generate a level of feedback that renders them self‐sustaining. In this case, the network can get stuck in a disorder state. The network theory holds that this is a general feature of mental disorders, which can therefore be understood as alternative stable states of strongly connected symptom networks. This idea naturally leads to a comprehensive model of psychopathology, encompassing a common explanatory model for mental disorders, as well as novel definitions of associated concepts such as mental health, resilience, vulnerability and liability. In addition, the network theory has direct implications for how to understand diagnosis and treatment, and suggests a clear agenda for future research in psychiatry and associated disciplines.     

Network-Based Interpretation of Genomic Variation Data

Advances in sequencing technologies are allowing genome-wide association studies at an ever-growing scale. The interpretation of these studies requires dealing with statistical and combinatorial challenges, owing to the multi-factorial nature of human diseases and the huge space of genomic markers that are being monitored. Recently, it was proposed that using protein–protein interaction network information could help in tackling these challenges by restricting attention to markers or combinations of markers that map to close proteins in the network. In this review, we survey techniques for integrating genomic variation data with network information to improve our understanding of complex diseases and reveal meaningful associations.     

Finding the “Dark Matter” in Human and Yeast Protein Network Prediction and Modelling

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or “dark matter” of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions.     

A Novel Analytical Framework for Dissecting the Genetic Architecture of Behavioral Symptoms in Neuropsychiatric Disorders

Background

For diagnosis of neuropsychiatric disorders, a categorical classification system is often utilized as a simple way for conceptualizing an often complex clinical picture. This approach provides an unsatisfactory model of mental illness, since in practice patients do not conform to these prototypical diagnostic categories. Family studies show notable familial co-aggregation between schizophrenia and bipolar illness and between schizoaffective disorders and both bipolar disorder and schizophrenia, revealing that mental illness does not conform to such categorical models and is likely to follow a continuum encompassing a spectrum of behavioral symptoms.

Results and Methodology

We introduce an analytic framework to dissect the phenotypic heterogeneity present in complex psychiatric disorders based on the conceptual paradigm of a continuum of psychosis. The approach identifies subgroups of behavioral symptoms that are likely to be phenotypically and genetically homogenous. We have evaluated this approach through analysis of simulated data with simulated behavioral traits and predisposing genetic factors. We also apply this approach to a psychiatric dataset of a genome scan for schizophrenia for which extensive behavioral information was collected for each individual patient and their families. With this approach, we identified significant evidence for linkage among depressed individuals with two distinct symptom profiles, that is individuals with sleep disturbance symptoms with linkage on chromosome 2q13 and also a mutually exclusive group of individuals with symptoms of concentration problems with linkage on chromosome 2q35. In addition we identified a subset of individuals with schizophrenia defined by language disturbances with linkage to chromosome 2p25.1 and a group of patients with a phenotype intermediate between those of schizophrenia and schizoaffective disorder with linkage to chromosome 2p21.

Conclusions

The findings presented are novel and demonstrate the efficacy of this approach in detection of genes underlying such complex human disorders as schizophrenia and depression.     

Competing Constructivisms: The Negotiation of PTSD and Related Stigma Among Post-9/11 Veterans in New York City

Posttraumatic stress disorder (PTSD) stands as a form of psychopathology that straddles moral and psychiatric domains. Grounded in discrete instances of trauma, PTSD represents an etiological outlier in an era of increased attention to the genetics of mental illness and a prime location for social constructivist analyses of mental illness. This examination of PTSD narratives—as voiced in qualitative interviews and focus groups with 50 veterans of the recent Iraq and Afghanistan wars living in New York City—attends to the processes through which veterans conceive and navigate PTSD symptoms and diagnoses. In so doing we highlight the social constructivist positions undertaken by veterans themselves as they varyingly challenge and internalize symptomology in dialogue with psychiatric definitions and the stigma associated with PTSD. Findings demonstrate the rejection of classic psychopathological etiology—in brain disease, for example—by many veterans as well as the complex balancing of benefit and stigma that veterans undertake when making decisions about presenting to psychiatric clinicians. Drawing on veterans’ accounts, we argue for greater cultural specificity in characterizing the diagnosis-seeking behavior of trauma survivors and a greater appreciation for the contradictions and compromise related to both acceptance and rejection of a mental health diagnosis.     

Convergent and Divergent Functional Connectivity Patterns in Schizophrenia and Depression

Major depression and schizophrenia are two of the most serious psychiatric disorders and share similar behavioral symptoms. Whether these similar behavioral symptoms underlie any convergent psychiatric pathological mechanisms is not yet clear. To address this issue, this study sought to investigate the whole-brain resting-state functional magnetic resonance imaging (MRI) of major depression and schizophrenia by using multivariate pattern analysis. Thirty-two schizophrenic patients, 19 major depressive disorder patients and 38 healthy controls underwent resting-state functional MRI scanning. A support vector machine in conjunction with intrinsic discriminant analysis was used to solve the multi-classification problem, resulting in a correct classification rate of 80.9% via leave-one-out cross-validation. The depression and schizophrenia groups both showed altered functional connections associated with the medial prefrontal cortex, anterior cingulate cortex, thalamus, hippocampus, and cerebellum. However, the prefrontal cortex, amygdala, and temporal poles were found to be affected differently by major depression and schizophrenia. Our preliminary study suggests that altered connections within or across the default mode network and the cerebellum may account for the common behavioral symptoms between major depression and schizophrenia. In addition, connections associated with the prefrontal cortex and the affective network showed promise as biomarkers for discriminating between the two disorders.     

Child and Parent Psychological Problems Influence Child Weight Control

This study used path analytic methods to assess the influence of child psychopathology measured using the Child Behavior Checklist and parent psychiatric symptoms measured using the Cornell Medical Index on changes in child percent overweight in obese 8- to 11-year-old children over 2 years (6 months of treatment, 18 months of follow-up) provided family-based behavioral intervention. Path analysis for the 0–6 month change showed the influence of mother and father psychiatric symptoms and child age on child anxiety/depression scores that in turn influenced child percent overweight change during treatment, accounting for 41 % of the shared variance. The path analysis for follow-up change showed mother and father psychiatric symptoms influenced child social problems at the first stage, and child social problems influenced child percent overweight change, accounting for 68.5% of the shared variance. These results provide the first demonstration that both parent and child problems may influence the short- and long-term success of obese children who participate in family-based behavioral treatment programs.     

SNMDA: A novel method for predicting microRNA‐disease associations based on sparse neighbourhood

miRNAs are a class of small noncoding RNAs that are associated with a variety of complex biological processes. Increasing studies have shown that miRNAs have close relationships with many human diseases. The prediction of the associations between miRNAs and diseases has thus become a hot topic. Although traditional experimental methods are reliable, they could only identify a limited number of associations as they are time‐consuming and expensive. Consequently, great efforts have been made to effectively predict reliable disease‐related miRNAs based on computational methods. In this study, we present a novel approach to predict the potential microRNA‐disease associations based on sparse neighbourhood. Specifically, our method takes advantage of the sparsity of the miRNA‐disease association network and integrates the sparse information into the current similarity matrices for both miRNAs and diseases. To demonstrate the utility of our method, we applied global LOOCV, local LOOCV and five‐fold cross‐validation to evaluate our method, respectively. The corresponding AUCs are 0.936, 0.882 and 0.934. Three types of case studies on five common diseases further confirm the performance of our method in predicting unknown miRNA‐disease associations. Overall, results show that SNMDA can predict the potential associations between miRNAs and diseases effectively.     

The nature of psychiatric disorders

A foundational question for the discipline of psychiatry is the nature of psychiatric disorders. What kinds of things are they? In this paper, I review and critique three major relevant theories: realism, pragmatism and constructivism. Realism assumes that the content of science is real and independent of human activities. I distinguish two “flavors” of realism: chemistry‐based, for which the paradigmatic example is elements of the periodic table, and biology‐based, for which the paradigm is species. The latter is a much better fit for psychiatry. Pragmatism articulates a sensible approach to psychiatric disorders just seeking categories that perform well in the world. But it makes no claim about the reality of those disorders. This is problematic, because we have a duty to advocate for our profession and our patients against other physicians who never doubt the reality of the disorders they treat. Constructivism has been associated with anti‐psychiatry activists, but we should admit that social forces play a role in the creation of our diagnoses, as they do in many sciences. However, truly socially constructed psychiatric disorders are rare. I then describe powerful arguments against a realist theory of psychiatric disorders. Because so many prior psychiatric diagnoses have been proposed and then abandoned, can we really claim that our current nosologies have it right? Much of our current nosology arose from a series of historical figures and events which could have gone differently. If we re‐run the tape of history over and over again, the DSM and ICD would not likely have the same categories on every iteration. Therefore, we should argue more confidently for the reality of broader constructs of psychiatric illness rather than our current diagnostic categories, which remain tentative. Finally, instead of thinking that our disorders are true because they correspond to clear entities in the world, we should consider a coherence theory of truth by which disorders become more true when they fit better into what else we know about the world. In our ongoing project to study and justify the nature of psychiatric disorders, we ought to be broadly pragmatic but not lose sight of an underlying commitment, despite the associated difficulties, to the reality of psychiatric illness.