Abstract Note

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In the past decades, reductionism has dominated both research directions and funding policies in clinical psychology and psychiatry. However, the intense search for the biological basis of mental disorders has not resulted in conclusive reductionist explanations of psychopathology. Recently, network models have been proposed as an alternative framework for the analysis of mental disorders, in which mental disorders arise from the causal interplay between symptoms. In this paper, we show that this conceptualization can help understand why reductionist approaches in psychiatry and clinical psychology are on the wrong track. First, symptom networks preclude the identification of a common cause of symptomatology with a neurobiological condition, because in symptom networks there is no such common cause. Second, symptom network relations depend on the content of mental states and as such feature intentionality. Third, the strength of network relations is highly likely to partially depend

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on cultural and historical contexts as well as external mechanisms in the environment. Taken together, these properties suggest that, if mental disorders are indeed networks of causally related symptoms, reductionist accounts cannot achieve the level of success associated with reductionist disease models in modern medicine. As an alternative strategy, we propose to interpret network structures in terms of D. C. Dennett's (1987) notion of real patterns, and suggest that, instead of being reducible to a biological basis, mental disorders feature biological and psychological factors that are deeply intertwined in feedback loops. This suggests that neither psychological nor biological levels can claim causal or explanatory priority, and that a holistic research strategy is necessary for progress in the study of mental disorders.

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APA PsycNet DoiLanding page

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"Philosophical Issues in Psychiatry III" published on by Oxford University Press.

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Schizophrenia is a very complex syndrome that involves widespread brain multi-dysconnectivity. Neural circuits within specific brain regions and their links to corresponding regions are abnormal in the illness. Theoretical models of dysconnectivity and the investigation of connectomics and brain network organization have been examined in schizophrenia since the early nineteenth century. In more recent years, advancements have been achieved with the development of neuroimaging tools that have provided further clues to the structural and functional organization of the brain and global neural networks in the illness. Neural circuitry that extends across prefrontal, temporal and parietal areas of the cortex as well as limbic and other subcortical brain regions is disrupted in schizophrenia. As a result, many patients have a poor response to antipsychotic treatment and treatment failure is common. Treatment resistance that is specific to positive, negative, and cognitive domains of the

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illness may be related to distinct circuit phenotypes unique to treatment-refractory disease. Currently, there are no customized neural circuit-specific and targeted therapies that address this neural dysconnectivity. Investigation of targeted therapeutics that addresses particular areas of substantial regional dysconnectivity is an intriguing approach to precision medicine in schizophrenia. This review examines current findings of system and circuit-level brain dysconnectivity in treatment-resistant schizophrenia based on neuroimaging studies. Within a connectome context, on-off circuit connectivity synonymous with excitatory and inhibitory neuronal pathways is discussed. Mechanistic cellular, neurochemical and molecular studies are included with specific emphasis given to cell pathology and synaptic communication in glutamatergic and GABAergic systems. In this review we attempt to deconstruct how augmenting treatments may be applied within a circuit context to improve circuit

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integration and treatment response. Clinical studies that have used a variety of glutamate receptor and GABA interneuron modulators, nitric oxide-based therapies and a variety of other strategies as augmenting treatments with antipsychotic drugs are included. This review supports the idea that the methodical mapping of system-level networks to both on (excitatory) and off (inhibitory) cellular circuits specific to treatment-resistant disease may be a logical and productive approach in directing future research toward the advancement of targeted pharmacotherapeutics in schizophrenia.

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Schizophrenia (SCZ) is a common complex disorder with poorly understood mechanisms and no effective drug treatments. Despite the high prevalence and vast unmet medical need represented by the disease, many drug companies have moved away from the development of drugs for SCZ. Therefore, alternative strategies are needed for the discovery of truly innovative drug treatments for SCZ. Here, we present a disease phenome-driven computational drug repositioning approach for SCZ. We developed a novel drug repositioning system, PhenoPredict, by inferring drug treatments for SCZ from diseases that are phenotypically related to SCZ. The key to PhenoPredict is the availability of a comprehensive drug treatment knowledge base that we recently constructed. PhenoPredict retrieved all 18 FDA-approved SCZ drugs and ranked them highly (recall=1.0, and average ranking of 8.49%). When compared to PREDICT, one of the most comprehensive drug repositioning systems currently available, in novel predictions,

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PhenoPredict represented clear improvements over PREDICT in Precision-Recall (PR) curves, with a significant 98.8% improvement in the area under curve (AUC) of the PR curves. In addition, we discovered many drug candidates with mechanisms of action fundamentally different from traditional antipsychotics, some of which had published literature evidence indicating their treatment benefits in SCZ patients. In summary, although the fundamental pathophysiological mechanisms of SCZ remain unknown, integrated systems approaches to studying phenotypic connections among diseases may facilitate the discovery of innovative SCZ drugs.

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Since the establishment of the symptoms-based categories in the Diagnostic and Statistical Manual of Mental Disorders (DSM), Third Edition, sociologists have raised concerns about the DSM’s failure to appreciate social, contextual factors when defining mental disorders. The author describes recent developments in psychiatric nosology—the DSM-5 revision process and the emergence of the Research Domain Criteria (RDoC)—and then considers their implications for decontextualization. Drawing on in-depth interviews with psychiatrists involved in the DSM-5 controversy and a content analysis of key documents, the author first recounts the ambitious DSM-5 revisions, illuminating the DSM-5 Task Force’s embrace of dimensionalization as a solution to the problem of validity and the ultimate rejection of this ‘‘paradigm shift’’ by psychiatrists. The Task Force’s failures prompted the National Institute of Mental Health to promote RDoC as an alternative nosological framework that eschews DSM

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categories altogether. Next, the author explores the ramifications of these events for decontextualization, which neither DSM-5 nor RDoC explicitly addresses, demonstrating how RDoC is poised to escalate decontextualization through its brain-centric conceptualization of mental disorders. To counteract these developments, sociologists should continue to promote ways of defining mental distress that underscore its social embeddedness.

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Purpose – Evidence-based medicine (EBM) is a technical and scientific paradigm in clinical practice that has delivered major improvements in the outcome of care in medicine and surgery. However, its value in psychiatry is much less clear. The purpose of the paper is thus to examine its value by subjecting empirical evidence from EBM to a conceptual analysis using the philosophy of Thomas Kuhn. Design/methodology/approach – The authors examine evidence drawn from meta-analyses of RCTs investigating the efficacy of specific treatments for depression in the form of antidepressant drugs and CBT. This shows that the non-specific aspects of treatment, the placebo effect and the quality of the therapeutic alliance as seen by the patient, are more important in determining outcome than the specific elements (active drug, specific therapeutic elements of CBT).

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Explanatory pluralism is the view that the best form and level of explanation depends on the kind of question one seeks to answer by the explanation, and that in order to answer all questions in the best way possible, we need more than one form and level of explanation. In the first part of this article, we argue that explanatory pluralism holds for the medical sciences, at least in theory. However, in the second part of the article we show that medical research and practice is actually not fully and truly explanatory pluralist yet. Although the literature demonstrates a slowly growing interest in non-reductive explanations in medicine, the dominant approach in medicine is still methodologically reductionist. This implies that non-reductive explanations often do not get the attention they deserve. We argue that the field of medicine could benefit greatly by reconsidering its reductive tendencies and becoming fully and truly explanatory pluralist. Nonetheless, trying to achieve the

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right balance in the search for and application of reductive and non-reductive explanations will in any case be a difficult exercise.

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Explanations of psychological phenomena seem to generate more public interest when they contain neuroscientific information. Even irrelevant neuroscience information in an explanation of a psychological phenomenon may interfere with people’s abilities to critically consider the underlying logic of this explanation. We tested this hypothesis by giving naïve adults, students in a neuroscience course, and neuroscience experts brief descriptions of psychological phenomena followed by one of four types of explanation, according to a 2 (good explanation vs. bad explanation) × 2 (without neuroscience vs. with neuroscience) design. Crucially, the neuroscience information was irrelevant to the logic of the explanation, as confirmed by the expert subjects. Subjects in all three groups judged good explanations as more satisfying than bad ones. But subjects in the two nonexpert groups additionally judged that explanations with logically irrelevant neuroscience information were more satisfying

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than explanations without. The neuroscience information had a particularly striking effect on nonexperts’ judgments of bad explanations, masking otherwise salient problems in these explanations.

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Physicians and other medical practitioners make untold numbers of judgments about patient care on a daily, weekly, and monthly basis. These judgments fall along a number of spectrums, from the mundane to the tragic, from the obvious to the challenging. Under the rubric of evidence-based medicine, these judgments will be informed by the robust conclusions of medical research. In the ideal circumstance medical research makes the best decision obvious to the trained professional. Even when practice approximates this ideal, it does so unevenly. Judgments in medical practice are always accompanied by uncertainty, and this uncertainty is a fickle companion—constant in its presence but inconstant in its expression. This feature of medical judgments gives rise to the moral responsibility of medical practitioners to be epistemically humble. This requires the recognition and communication of the uncertainty that accompanies their judgment as well as a commitment to avoiding intuitive

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innovations.

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Despite high prevalence and enormous unmet medical need, the pharmaceutical industry has recently de-emphasized neuropsychiatric disorders as ‘too difficult’ a challenge to warrant major investment. Here I describe major obstacles to drug discovery and development including a lack of new molecular targets, shortcomings of current animal models, and the lack of biomarkers for clinical trials. My major focus, however, is on new technologies and scientific approaches to neuropsychiatric disorders that give promise for revitalizing therapeutics and may thus answer industry’s concerns.

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In this article, we examine the different ways in which Thomas Szasz and Michel Foucault have challenged dominant perspectives within psychiatry. We identify, analyze, and compare the central elements of their respective discourses on psychiatry and show that although they are often bracketed together, in fact there are certain fundamental differences between Szasz and Foucault. Of most importance is their contrasting ways of characterizing the nature and role of critical thought. Whereas Szasz’s analysis is predicated on a number of binary distinctions, Foucault works to overcome such distinctions. In the past ten years, a new movement of critical psychiatry has emerged. Although this shares certain concerns with the critical psychiatry of the 1960s and 1970s, there are substantial differences. We argue that this discourse is more resonant with the Foucauldian approach.

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Reducing stigma is crucial for facilitating recovery from psychological problems. Viewing these problems biomedically may reduce the tendency to blame affected persons, but critics have cautioned that it could also increase other facets of stigma. We report on the first meta-analytic review of the effects of biogenetic explanations on stigma. A comprehensive search yielded 28 eligible experimental studies. Four separate meta-analyses (Ns = 1207–3469) assessed the effects of biogenetic explanations on blame, perceived dangerousness, social distance, and prognostic pessimism. We found that biogenetic explanations reduce blame (Hedges g = − 0.324) but induce pessimism (Hedges g = 0.263). We also found that biogenetic explanations increase endorsement of the stereotype that people with psychological problems are dangerous (Hedges g = 0.198), although this result could reflect publication bias. Finally, we found that biogenetic explanations do not typically affect social distance. Promoting

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biogenetic explanations to alleviate blame may induce pessimism and set the stage for self-fulfilling prophecies that could hamper recovery from psychological problems.

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People with serious mental illness get sick and die 10--20 years earlier than their same age cohort. The social determinants are many: stigma associated with mental illness, poverty, ethnicity-based discrimination, higher rates of smoking and alcohol and drug use, and poor diet and exercise patterns, to name a few. Although multiple interventions have emerged as ways to combat these health challenges, additional research is necessary for the continued development and evaluation of strategies. This context serves as the springboard for Health and Wellness in People Living With Serious Mental Illness. Through multiple case vignettes, the book delves into the challenges of health and wellness for people with mental illness -- including those listed above -- summarizing the research on mortality and morbidity in this group as well as information about the status quo on wellness. It also provides a thorough description of community-based participatory research (CBPR), an approach that

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includes people in a community as partners in all facets of research, rather than just the subjects of that research. CBPR acts as the lens through which this guide considers solutions to these health problems, including integrated services and patient-centered medical homes; medical practices that diminish the iatrogenic effects of psychiatry; psychoeducation; interpersonal supports; and shared decision-making. Co-edited by Patrick Corrigan, with a 30-year history in services research, and Sonya Ballentine, a community-based member of a CBPR team, this volume offers a grounded, real-world illustration of CBPR in practice. Students of psychiatry, practicing clinicians, primary care providers, allied health professionals, policy makers -- all will find, in the pages of this book, a nuanced portrait of the health challenges patients with mental illness face, possible treatment options, and future directions for the field.

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Antipsychotic drugs were incidentally discovered in the 1950s, but their mechanisms of action are still not understood. Better understanding of schizophrenia pathogenesis could shed light on actions of current drugs and reveal novel druggable pathways for unmet therapeutic needs. Recent genome-wide association studies offer unprecedented opportunities to characterize disease gene networks and uncover drug-disease relationships. Polygenic overlap between schizophrenia risk genes and antipsychotic drug targets has been demonstrated. However, the specific genes and pathways constituting this overlap are undetermined. Risk genes of polygenic disorders do not operate in isolation, but in combination with other genes. Thus, we utilized protein-protein interaction networks (interactome) to map antipsychotic drug targets (n=88) to networks of schizophrenia risk genes (n=328). Our results showed that schizophrenia risk genes were significantly localized in the interactome (p=0.0015), forming a

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distinct disease module. Core genes of the module were enriched for genes involved in developmental biology and cognition, which may have a central role in schizophrenia etiology. Intriguingly, antipsychotic drug targets overlapped with the core disease module and comprised multiple pathways beyond dopamine. Some important risk genes like CHRN, PCDH and HCN families were not connected to existing antipsychotics, but may be suitable targets for novel drugs or drug repurposing opportunities to treat other aspects of schizophrenia such as cognitive dysfunction and negative symptoms. This network medicine approach provides a platform to collate information of disease genetics and drug-gene interactions to shift focus from development of antipsychotics to multi-target anti-schizophrenia drugs. This approach is transferable to other diseases.

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OBJECTIVE: Computational drug repositioning can lead to a considerable reduction in cost and time in any drug development process. Recent approaches have addressed the network-based nature of biological information for performing complex prioritization tasks. In this work, we propose a new methodology based on heterogeneous network prioritization that can aid researchers in the drug repositioning process. METHODS: We have developed DrugNet, a new methodology for drug-disease and disease-drug prioritization. Our approach is based on a network-based prioritization method called ProphNet which has recently been developed by the authors. ProphNet is able to integrate data from complex networks involving a wide range of types of elements and interactions. In this work, we built a network of interconnected drugs, proteins and diseases and applied DrugNet to different types of tests for drug repositioning. RESULTS: We tested the performance of our approach on different validation tests,

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including cross validation and tests based on real clinical trials. DrugNet achieved a mean AUC value of 0.9552±0.0015 in 5-fold cross validation tests, and a mean AUC value of 0.8364 for tests based on recent clinical trials (phases 0-4) not present in our data. These results suggest that DrugNet could be very useful for discovering new drug uses. We also studied specific cases of particular interest, proving the benefits of heterogeneous data integration in this problem. CONCLUSIONS: Our methodology suggests that new drugs can be repositioned by generating ranked lists of drugs based on a given disease query or vice versa. Our study shows that the simultaneous integration of information about diseases, drugs and targets can lead to a significant improvement in drug repositioning tasks. DrugNet is available as a web tool from http://genome2.ugr.es/drugnet/ (accessed 23.09.14). Matlab source code is also available on the website.

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Until recently, it was thought that there is no neuronal regeneration and neuron loss is irreversible, but today the exist-ence of neural regeneration and neural plasticity have been documented. The effectiveness of stem cell treatment in nu-merous degenerative diseases, as well as some neurodegenerative diseases, has created hopes toward the use of stem cell treatment in schizophrenia, which is a disease that progresses with neuronal degeneration and loss of neurons, and is characterized with worsening clinical outcomes and impairment.

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The communication of uncertainty in clinical evidence is an important endeavor that poses difficult conceptual, methodological, and ethical problems. Conceptual problems include logical paradoxes in the meaning of probability and “ambiguity”— second-order uncertainty arising from the lack of reliability, credibility, or adequacy of probability information. Methodological problems include questions about optimal methods for representing fundamental uncertainties and for communicating these uncertainties in clinical practice. Ethical problems include questions about whether communicating uncertainty enhances or diminishes patient autonomy and produces net benefits or harms. This article reviews the limited but growing literature on these problems and efforts to address them and identifies key areas of focus for future research. It is argued that the critical need moving forward is for greater conceptual clarity and consistent representational methods that make the meaning of various

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uncertainties understandable, and for clinical interventions to support patients in coping with uncertainty in decision making.

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Uncertainty is an inherent part of knowledge, and yet in an era of contested expertise, many shy away from openly communicating their uncertainty about what they know, fearful of their audience's reaction. But what effect does communication of such epistemic uncertainty have? Empirical research is widely scattered across many disciplines. This interdisciplinary review structures and summarizes current practice and research across domains, combining a statistical and psychological perspective. This informs a framework for uncertainty communication in which we identify three objects of uncertainty—facts, numbers and science—and two levels of uncertainty: direct and indirect. An examination of current practices provides a scale of nine expressions of direct uncertainty. We discuss attempts to codify indirect uncertainty in terms of quality of the underlying evidence. We review the limited literature about the effects of communicating epistemic uncertainty on cognition, affect, trust and

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decision-making. While there is some evidence that communicating epistemic uncertainty does not necessarily affect audiences negatively, impact can vary between individuals and communication formats. Case studies in economic statistics and climate change illustrate our framework in action. We conclude with advice to guide both communicators and future researchers in this important but so far rather neglected field.

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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

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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.

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BindingDB, www.bindingdb.org, is a publicly accessible database of experimental protein-small molecule interaction data. Its collection of over a million data entries derives primarily from scientific articles and, increasingly, US patents. BindingDB provides many ways to browse and search for data of interest, including an advanced search tool, which can cross searches of multiple query types, including text, chemical structure, protein sequence and numerical affinities. The PDB and PubMed provide links to data in BindingDB, and vice versa; and BindingDB provides links to pathway information, the ZINC catalog of available compounds, and other resources. The BindingDB website offers specialized tools that take advantage of its large data collection, including ones to generate hypotheses for the protein targets bound by a bioactive compound, and for the compounds bound by a new protein of known sequence; and virtual compound screening by maximal chemical similarity, binary kernel

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discrimination, and support vector machine methods. Specialized data sets are also available, such as binding data for hundreds of congeneric series of ligands, drawn from BindingDB and organized for use in validating drug design methods. BindingDB offers several forms of programmatic access, and comes with extensive background material and documentation. Here, we provide the first update of BindingDB since 2007, focusing on new and unique features and highlighting directions of importance to the field as a whole.

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Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term,

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where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

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Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Despite the continuous information accumulation of clinically significant DDIs, there are few open-access knowledge systems dedicated to the curation of DDI associations. To facilitate the clinicians to screen for dangerous drug combinations and improve health systems, we present DDInter, a curated DDI database with comprehensive data, practical medication guidance, intuitive function interface, and powerful visualization to the scientific community. Currently, DDInter contains about 0.24M DDI associations connecting 1833 approved drugs (1972 entities). Each drug is annotated with basic chemical and pharmacological information and its interaction network. For DDI associations, abundant and professional annotations are provided, including severity, mechanism description, strategies for managing potential side effects, alternative medications, etc. The drug

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entities and interaction entities are efficiently cross-linked. In addition to basic query and browsing, the prescription checking function is developed to facilitate clinicians to decide whether drugs combinations can be used safely. It can also be used for informatics-based DDI investigation and evaluation of other prediction frameworks. We hope that DDInter will prove useful in improving clinical decision-making and patient safety. DDInter is freely available, without registration, at http://ddinter.scbdd.com/.

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Psychedelics have inspired new hope for treating brain disorders, as they seem to be unlike any treatments currently available. Not only do they produce sustained therapeutic effects following a single administration, they also appear to have broad therapeutic potential, demonstrating efficacy for treating depression, post-traumatic stress disorder (PTSD), anxiety disorders, substance abuse disorder, and alcohol use disorder, among others. Psychedelics belong to a more general class of compounds known as psychoplastogens, which robustly promote structural and functional neural plasticity in key circuits relevant to brain health. Here we discuss the importance of structural plasticity in the treatment of neuropsychiatric diseases, as well as the evidence demonstrating that psychedelics are among the most effective chemical modulators of neural plasticity studied to date. Furthermore, we provide a theoretical framework with the potential to explain why psychedelic compounds produce

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long-lasting therapeutic effects across a wide range of brain disorders. Despite their promise as broadly efficacious neurotherapeutics, there are several issues associated with psychedelic-based medicines that drastically limit their clinical scalability. We discuss these challenges and how they might be overcome through the development of non-hallucinogenic psychoplastogens. The clinical use of psychedelics and other psychoplastogenic compounds marks a paradigm shift in neuropsychiatry toward therapeutic approaches relying on the selective modulation of neural circuits with small molecule drugs. Psychoplastogen research brings us one step closer to actually curing mental illness by rectifying the underlying pathophysiology of disorders like depression, moving beyond simply treating disease symptoms. However, determining how to most effectively deploy psychoplastogenic medicines at scale will be an important consideration as the field moves forward.

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[ai_questions]

What is the most effective treatment for patients with schizophrenia?

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[ai_questions]

What is the most effective treatment for patients with schizophrenia?

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[ai_questions]

What are the most recent evidence-based approaches for treating drug-resistant diseases in developing communities?

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[ai_questions]

What is the evidence for repositioning drugs for scz in recent years?

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[ai_questions]

What are the most recent evidence-based approaches to repositioning drugs for the treatment of scz?

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[ai_ideas]

One possible innovative idea is to develop a community-based approach to drug development and testing. This approach would involve engaging community members in the design and implementation of clinical trials, as well as in the development and testing of new treatments. This approach would benefit from the expertise of both community members and medical

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[ai_ideas]

The idea is to develop a community-based approach to testing new drugs for scz. The community would be involved in every step of the process, from drug development to target selection to testing. This would ensure that the community is invested in the success of the project and that the most effective treatments

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[ai_ideas]

The development of a community-based approach to testing new drugs for scz.

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[ai_ideas]

A recent study found that drug repositioning – the process of finding new uses for existing drugs – is a promising approach for developing new treatments for diseases with high unmet medical needs, such as scz. The study's findings provide evidence that repositioning drugs from other therapeutic fields may be

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