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A "natural kind" is a specific classification that identifies some structure of truth and reality, a delimited entity. Psychiatric disorders are not natural kinds. As one moves from physics and chemistry to biology and medicine, natural kinds degrade, and the boundaries of differentiating phenomena become less clear. Within psychiatry, the categorization of psychopathology has further ontological challenges, especially across development. We suggest that to identify and isolate clinical subgroups, it is critical to integrate external validators in an iterative process, with the goal of linking classification to treatments with maximal clinical benefit. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

This is an invited commentary article for the special issue. The main thesis is that an effective strategy for computational psychiatry to handle the (possibly intrinsic) heterogeneity of psychiatric disorders is to focus on developing clinical principles rather than solely precision medicine. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Despite predictions that neuroscientific discoveries would revolutionize psychiatry, decades of research have not yet led to clinically significant advances in psychiatric care. For this reason, an increasing number of researchers are recognizing the limitations of a purely biomedical approach in psychiatric research. These researchers call for reevaluating the conceptualization of mental disorders and argue for a non-reductionist approach to mental health. The aim of this paper is to discuss philosophical assumptions that underly neuroscientific research in psychiatry and offer practical tools to researchers for overcoming potential conceptual problems that are derived from those assumptions. Specifically, we will discuss: the analogy problem, questioning whether mental health problems are equivalent to brain disorders, the normativity problem, addressing the value-laden nature of psychiatric categories and the priority problem, which describes the level of analysis (e.g., biological, psychological, social, etc.) that should be prioritized when studying psychiatric conditions. In addition, we will explore potential strategies to mitigate practical problems that might arise due to these implicit assumptions. Overall, the aim of this paper is to suggest philosophical tools of practical use for neuroscientists, demonstrating the benefits of a closer collaboration between neuroscience and philosophy., (© 2024 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Substance use disorder (SUD) is common in psychiatric patients and has a negative impact on health and well-being. However, SUD often goes untreated, and there is a need for psychiatrists, of all specialties, to address this pervasive clinical problem. In this review, the authors' goal is to provide a resource that describes treatments for SUD, using neuroscience as a framework. They discuss the effect of pharmacotherapy on craving, intoxication, and withdrawal and its ability to interrupt the cycle of substance use in SUD. The neuroscience of stress is reviewed, including medications targeting neurotransmitter systems activated by alarm and fear. Neuroplasticity and promising treatments that use this mechanism, including ketamine, psilocybin, and transcranial magnetic stimulation (TMS), are discussed. The authors conclude by listing resources and practice guidelines for physicians interested in learning more about treatments for SUD., Competing Interests: Dr. Levin has received grant support from the National Center for Advancing Translational Sciences, NIDA, SAMHSA, and US WorldMeds and research support from Aelis Pharmaceuticals; she has received medication from Indivior for research; she has served on the National Advisory Council on Alcohol Abuse and Alcoholism and as a nonpaid member of scientific advisory boards for Alkermes, Atai Life Science, Boehringer Ingelheim, Indivior, Novartis, Teva, and US WorldMeds; she has served as a consultant for Major League Baseball; and she receives royalties from APA Publishing. The other authors report no financial relationships with commercial interests.

To understand the complex nature of heterogeneous psychiatric disorders, scientists and clinicians are required to employ a wide range of clinical, endophenotypic, neuroimaging, genomic, and environmental data to understand the biological mechanisms of psychiatric illness before this knowledge is applied into clinical setting. Machine learning (ML) is an automated process that can detect patterns from large multidimensional datasets and can supersede conventional statistical methods as it can detect both linear and non-linear relationships. Due to this advantage, ML has potential to enhance our understanding, improve diagnosis, prognosis and treatment of psychiatric disorders. The current review provides an in-depth examination of, and offers practical guidance for, the challenges encountered in the application of ML models in psychiatric research and clinical practice. These challenges include the curse of dimensionality, data quality, the 'black box' problem, hyperparameter tuning, external validation, class imbalance, and data representativeness. These challenges are particularly critical in the context of psychiatry as it is expected that researchers will encounter them during the stages of ML model development and deployment. We detail practical solutions and best practices to effectively mitigate the outlined challenges. These recommendations have the potential to improve reliability and interpretability of ML models in psychiatry., Competing Interests: Declaration of competing interest All authors declare that they have no conflicts of interest., (Copyright © 2024. Published by Elsevier B.V.)

Importance: Advancing precision psychiatry, where treatments are based on an individual's biology rather than solely their clinical presentation, requires attention to several key attributes for any candidate biomarker. These include test-retest reliability, sensitivity to relevant neurophysiology, cost-effectiveness, and scalability. Unfortunately, these issues have not been systematically addressed by biomarker development efforts that use common neuroimaging tools like magnetic resonance imaging (MRI) and electroencephalography (EEG). Here, the critical barriers that neuroimaging methods will need to overcome to achieve clinical relevance in the near to intermediate term are examined., Observations: Reliability is often overlooked, which together with sensitivity to key aspects of neurophysiology and replicated predictive utility, favors EEG-based methods. The principal barrier for EEG has been the lack of large-scale data collection among multisite psychiatric consortia. By contrast, despite its high reliability, structural MRI has not demonstrated clinical utility in psychiatry, which may be due to its limited sensitivity to psychiatry-relevant neurophysiology. Given the prevalence of structural MRIs, establishment of a compelling clinical use case remains its principal barrier. By contrast, low reliability and difficulty in standardizing collection are the principal barriers for functional MRI, along with the need for demonstration that its superior spatial resolution over EEG and ability to directly image subcortical regions in fact provide unique clinical value. Often missing, moreover, is consideration of how these various scientific issues can be balanced against practical economic realities of psychiatric health care delivery today, for which embedding economic modeling into biomarker development efforts may help direct research efforts., Conclusions and Relevance: EEG seems most ripe for near- to intermediate-term clinical impact, especially considering its scalability and cost-effectiveness. Recent efforts to broaden its collection, as well as development of low-cost turnkey systems, suggest a promising pathway by which neuroimaging can impact clinical care. Continued MRI research focused on its key barriers may hold promise for longer-horizon utility.

Neuroimaging is a major tool that holds immense translational potential for understanding psychiatric disorder phenomenology and treatment. However, although epidemiological and social research highlights the many ways inequity and representativeness influences mental health, there is a lack of consideration of how such issues may impact neuroimaging features in psychiatric research. More specifically, the potential extent to which racialized inequities may affect underlying neurobiology and impact the generalizability of neural models of disorders is unclear. The present review synthesizes research focused on understanding the potential consequences of racial/ethnic inequities relevant to neuroimaging in psychiatry. We first discuss historical and contemporary drivers of inequities that persist today. We then discuss the neurobiological consequences of these inequities as revealed through current research, and note emergent research demonstrating the impact such inequities have on our ability to use neuroimaging to understand psychiatric disease. We end with a set of recommendations and practices to move the field towards more equitable approaches that will advance our abilities to develop truly generalizable neurobiological models of psychiatric disorders., (© 2024. The Author(s), under exclusive licence to American College of Neuropsychopharmacology.)

Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

It is little appreciated not only how closely linked are the disciplines of neurology, psychiatry, and anthropology but even more so the degree to which they share a "common ancestry". This paper briefly reviews the definition and historical origins of each area of study to then begin to illustrate how their "genealogies" overlap. This illustration is by way of a sampling of the many key figures who contributed to the rise of not just neurology, psychiatry or anthropology but of all three disciplines. That is, a selective review is undertaken of paragons whose careers bridged medicine, neuropsychiatry, and anthropology. A sampling from among the dozens who have made major contributions to spanning these disciplines illuminates the significant extent of their co-mingled intellectual ancestry. This series is akin to a data table of necessarily concise biographical vignettes - past scholars with some or full medical training who also advanced both anthropology and neuropsychiatry as these disciplines grew into intellectual maturity. Each is, in a sense, a data point that bolsters the overarching thesis that the intellectual history of these disciplines have shared ancestry. Thus, even this preliminary and topical survey of a few past "exemplars" underscores the importance of this unique intellectual siblingship. Moreover, there is now a profusion of living scholars who add fulsomely to what might be deemed this 'trilateral marriage' of anthropology, psychiatry, and neurology. A compilation of more contemporary contributors is well worthy of a future review that expands from this first consideration. This initial work is meant to engender more robust scholarship that better elucidates and, thereby, enriches and enlivens further work while also uncovering new avenues of deeper insight, notably as to the "conceptual and heuristic progression" of evolutionary neurosciences with respect to the normative and pathologic., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Although the lifetime burden due to mental disorders is increasing, we lack tools for more precise diagnosing and treating prevalent and disabling disorders such as major depressive disorder. We lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry, focusing on major depressive and anxiety disorders. We begin by outlining evidence for the use of functional neuroimaging to stratify the heterogeneity of these disorders, based on underlying circuit dysfunction. We then review the current landscape of how functional neuroimaging-derived circuit predictors can predict treatment outcomes and clinical trajectories in depression and anxiety. Future directions for advancing clinically appliable neuroimaging measures are considered. We conclude by considering the opportunities and challenges of translating neuroimaging measures into practice. As an illustration, we highlight one approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation., (© 2024. The Author(s), under exclusive licence to American College of Neuropsychopharmacology.)

Precision functional mapping (PFM) is a neuroimaging approach to reliably estimate metrics of brain function from individual people via the collection of large amounts of fMRI data (hours per person). This method has revealed much about the inter-individual variation of functional brain networks. While standard group-level studies, in which we average brain measures across groups of people, are important in understanding the generalizable neural underpinnings of neuropsychiatric disorders, many disorders are heterogeneous in nature. This heterogeneity often complicates clinical care, leading to patient uncertainty when considering prognosis or treatment options. We posit that PFM methods may help streamline clinical care in the future, fast-tracking the choice of personalized treatment that is most compatible with the individual. In this review, we provide a history of PFM studies, foundational results highlighting the benefits of PFM methods in the pursuit of an advanced understanding of individual differences in functional network organization, and possible avenues where PFM can contribute to clinical translation of neuroimaging research results in the way of personalized treatment in psychiatry., (© 2024. The Author(s), under exclusive licence to American College of Neuropsychopharmacology.)

Neuroimaging, across positron emission tomography (PET), electroencephalography (EEG), and magnetic resonance imaging (MRI), has been a mainstay of clinical neuroscience research for decades, yet has penetrated little into psychiatric drug development beyond often underpowered phase 1 studies, or into clinical care. Simultaneously, there is a pressing need to improve the probability of success in drug development, increase mechanistic diversity, and enhance clinical efficacy. These goals can be achieved by leveraging neuroimaging in a precision psychiatry framework, wherein effects of drugs on the brain are measured early in clinical development to understand dosing and indication, and then in later-stage trials to identify likely drug responders and enrich clinical trials, ultimately improving clinical outcomes. Here we examine the key variables important for success in using neuroimaging for precision psychiatry from the lens of biotechnology and pharmaceutical companies developing and deploying new drugs in psychiatry. We argue that there are clear paths for incorporating different neuroimaging modalities to de-risk subsequent development phases in the near to intermediate term, culminating in use of select neuroimaging modalities in clinical care for prescription of new precision drugs. Better outcomes through neuroimaging biomarkers, however, require a wholesale commitment to a precision psychiatry approach and will necessitate a cultural shift to align biopharma and clinical care in psychiatry to a precision orientation already routine in other areas of medicine., (© 2024. The Author(s).)

Neuromelanin-sensitive MRI is a burgeoning non-invasive neuroimaging method with an increasing number of applications in psychiatric research. This MRI modality is sensitive to the concentration of neuromelanin, which is synthesized from intracellular catecholamines and accumulates in catecholaminergic nuclei including the dopaminergic substantia nigra and the noradrenergic locus coeruleus. Emerging data suggest the utility of neuromelanin-sensitive MRI as a proxy measure for variability in catecholamine metabolism and function, even in the absence of catecholaminergic cell loss. Given the importance of catecholamine function to several psychiatric disorders and their treatments, neuromelanin-sensitive MRI is ideally positioned as an informative and easy-to-acquire catecholaminergic index. In this review paper, we examine basic aspects of neuromelanin and neuromelanin-sensitive MRI and focus on its psychiatric applications in the contexts of mechanistic research and biomarker development. We discuss ongoing debates and state-of-the-art research into the mechanisms of the neuromelanin-sensitive MRI contrast, standardized protocols and optimized analytic approaches, and application of cutting-edge methods such as machine learning and artificial intelligence to enhance the feasibility and predictive power of neuromelanin-sensitive-MRI-based tools. We finally lay out important future directions to allow neuromelanin-sensitive-MRI to fulfill its potential as a key component of the research, and ultimately clinical, toolbox in psychiatry., (© 2024. The Author(s), under exclusive licence to American College of Neuropsychopharmacology.)

Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis., (© 2024. The Author(s).)

Background: Genetic testing in psychiatry has gained attention, raising questions about its application and impact. Understanding stakeholders' perspectives, including healthcare providers and patients, is vital for informed policy development. The aim of this systematic review was to focus on the perceptions and concerns of patients and healthcare workers in psychiatry regarding the use of genetic testing., Methods: We conducted a systematic review following PRISMA guidelines, for the period 1/2/2014, to 1/1/2024, via PubMed and Embase databases identifying 50 articles in total. After excluding duplicates ( n  = 12), 38 articles went through screening. After careful full-text article assessment for eligibility and applying the inclusion and exclusion criteria, only fifteen ( n  = 15) of the articles were included., Results: Among 15 selected studies involving 3,156 participants (2,347 healthcare professionals; 809 patients), thematic analysis identified four primary themes: Organizational-implementation concerns, Ethical Considerations, Concerns on changes in clinical praxis, and Legal implications. Despite these concerns, seven out of eleven studies indicated that healthcare workers viewed genetic testing in psychiatry positively. Patients' perspectives varied, with two of the four studies reflecting positive attitudes. No pervasive negative sentiment was observed., Conclusion: Our review highlights the multidimensional perspectives of healthcare professionals and patients surrounding the application of genetic testing in psychiatry. These considerations need to be addressed to facilitate the implementation of genetic testing in clinical praxis in psychiatry. Further research is needed for validation of the results and to guide policies and clinicians in the integration of genetic testing into mental healthcare practice., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ioannou, Constantinidou and Chatzittofis.)

Introduction: Mobile health (mHealth) technologies, including smartphone apps and wearables, have improved health care by providing innovative solutions for monitoring, education and treatment, particularly in mental health., Method: This review synthesises findings from a series of reviews on mHealth interventions in psychiatry. Publications were systematically searched in PubMed, MEDLINE, PsycINFO, ScienceDirect, Scopus, Web of Science and Cochrane Library., Results: Out of 2147 records, 111 studies from 2014 to 2024 focusing on anxiety and depression were included. These studies highlight the effectiveness of mHealth interventions in reducing symptoms through cognitive-behavioural therapy, mindfulness and psychoeducation, benefitting adolescents, perinatal women and marginalised groups. Additionally, mHealth shows promise in managing substance use disorders and severe mental illnesses like schizophrenia, bipolar disorder and psychosis., Conclusion: Despite positive outcomes, challenges such as data privacy, user engagement and healthcare integration persist. Further robust trials and evidence-based research are needed to validate the efficacy of mHealth technologies., (Copyright © 2024 Copyright: © 2024 Singapore Medical Journal.)

Purpose of Review: We reviewed recent evidence regarding the impact of climate change (specifically, high ambient temperatures, heatwaves, weather-related disasters, and air pollution) on older adults' mental health. We also summarized evidence regarding other medical problems that can occur in aging adults in connection with climate change, resulting in psychiatric manifestations or influencing psychopharmacological management., Recent Findings: Older adults can experience anxiety, depressive, and/or posttraumatic stress symptoms, as well as sleep disturbances in the aftermath of climate disasters. Cognitive deficits may occur with exposure to air pollutants, heatwaves, or post-disaster. Individuals with major neurocognitive disorders and/or preexisting psychiatric illness have a higher risk of psychiatric hospitalizations after exposure to high temperatures and air pollution. There is a growing body of research regarding psychiatric clinical presentations associated with climate change in older adults. However, there is a paucity of evidence on management strategies. Future research should investigate culturally appropriate, cost-effective psychosocial and pharmacological interventions., (© 2024. The Author(s).)

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Introduction: Medical decision-making is crucial for effective treatment, especially in psychiatry where diagnosis often relies on subjective patient reports and a lack of high-specificity symptoms. Artificial intelligence (AI), particularly Large Language Models (LLMs) like GPT, has emerged as a promising tool to enhance diagnostic accuracy in psychiatry. This comparative study explores the diagnostic capabilities of several AI models, including Aya, GPT-3.5, GPT-4, GPT-3.5 clinical assistant (CA), Nemotron, and Nemotron CA, using clinical cases from the DSM-5., Methods: We curated 20 clinical cases from the DSM-5 Clinical Cases book, covering a wide range of psychiatric diagnoses. Four advanced AI models (GPT-3.5 Turbo, GPT-4, Aya, Nemotron) were tested using prompts to elicit detailed diagnoses and reasoning. The models' performances were evaluated based on accuracy and quality of reasoning, with additional analysis using the Retrieval Augmented Generation (RAG) methodology for models accessing the DSM-5 text., Results: The AI models showed varied diagnostic accuracy, with GPT-3.5 and GPT-4 performing notably better than Aya and Nemotron in terms of both accuracy and reasoning quality. While models struggled with specific disorders such as cyclothymic and disruptive mood dysregulation disorders, others excelled, particularly in diagnosing psychotic and bipolar disorders. Statistical analysis highlighted significant differences in accuracy and reasoning, emphasizing the superiority of the GPT models., Discussion: The application of AI in psychiatry offers potential improvements in diagnostic accuracy. The superior performance of the GPT models can be attributed to their advanced natural language processing capabilities and extensive training on diverse text data, enabling more effective interpretation of psychiatric language. However, models like Aya and Nemotron showed limitations in reasoning, indicating a need for further refinement in their training and application., Conclusion: AI holds significant promise for enhancing psychiatric diagnostics, with certain models demonstrating high potential in interpreting complex clinical descriptions accurately. Future research should focus on expanding the dataset and integrating multimodal data to further enhance the diagnostic capabilities of AI in psychiatry., Competing Interests: Declaration of Competing Interest The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript, (Copyright © 2024 Elsevier B.V. All rights reserved.)

The use of clinical prediction models to produce individualized risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implement them in routine clinical care. The current review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number needed to test). We review 4 externally validated clinical prediction models designed to predict psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models and the potential added value of integrating data from evidence syntheses, standardized psychometric assessments, and biological data into EHRs. Clinical prediction models can utilize routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g., meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve the performance of clinical prediction models., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

This article provides a comprehensive review of recent developments regarding a new atypical antipsychotic drug - cariprazine - considering the mechanism of action, efficacy, safety, and promising therapeutic option for various psychiatric disorders, including schizophrenia and bipolar disorder, therapy of addictions, and treatment in the pediatric population. Its distinct pharmacological profile, characterized by partial agonism at dopamine D2 and D3 receptors, as well as serotonin receptors - 5HT1A with a preference for the D3 receptor - sets it apart from other antipsychotics. The unique mechanism of action contributes to cariprazine's positive impact on negative symptoms in schizophrenia and an antidepressant effect. Its relatively low risk of adverse effects, such as sedation, metabolic issues, and hypotension, enhances its tolerability. In bipolar affective disorder, cariprazine exhibits effectiveness in managing both depressive and manic episodes. Ongoing research in pediatric populations suggests potential benefits in schizophrenia, bipolar I disorder, and autism spectrum disorder, but further research is necessary to establish safety and efficacy. Moreover, cariprazine shows promise in addiction therapy, particularly with coexisting psychiatric disorders. Continued research and clinical exploration may discover additional insights, broadening its use in diverse patient populations. This article aims to review the role of cariprazine, a dopamine D2/D3 and serotonin 5-HT1A receptor partial agonist, in the management of psychotic illnesses, including schizophrenia, bipolar disorder, addiction therapy, and pediatric treatment.

Response rate to treatment is generally not as high as expected in psychiatric disorders. The lack of clinical improvement under a well-conducted treatment, that complies with guidelines, may be the consequence of genetic abnormalities that impact the metabolizing pathways of the drug. Genetic polymorphism of metabolizing enzymes is frequent in the population and has been proven to have a clinical impact. It may also be the consequence of environmental or organic factors that interact with the pharmacokinetic pathways (absorption, distribution, metabolizing, excretion) of the drug. These intrinsic and extrinsic factors will lead to inter- and intraindividual fluctuations in plasma drug concentrations. Therapeutic drug monitoring permits to measure plasma drug concentrations in order to adapt psychopharmacotherapy individually. In some cases, it can be coupled to pharmacogenetic testings. This review presents recent literature and guidelines on the subject. Eventually, there is a focus made on the French-speaking part of Belgium where neither therapeutic drug monitoring, nor pharmacogenetics testing, are used frequently in clinical practice. Some challenges are to be addressed to implement these techniques in Belgium.

Background: In liaison psychiatry, issues related to multiple substance use disorders are encountered, but systematic assessment and treatment protocols are lacking. The implementation of psychoeducation groups with earlier access could be one solution, but it raises the question of how to systematically approach psychoeducation for multiple substance use disorders., Subject and Method: Based on a narrative review of multiple substance use disorders in liaison psychiatry and the proposed treatments, we aimed to identify the possible systematisation of standardised psychoeducation., Results: The triadic model of interoception-impulsivity-addiction emerged as a general approach to addressing addiction issues and could serve as a foundation for the standardisation of psychoeducation., Conclusion: Prospective studies on the impact of systematised brief psychoeducational interventions on multiple substance use disorders in the patient treatment plan are to be developed.

Psychiatry has the historical opportunity to shape the future of mental health care, medicine, and society. The term New Brave Psychiatry refers to challenges to psychiatry in our volatile, uncertain, complex and ambiguous (VUCA) world. The present and the future of psychiatry lie on the recognizing these challenges and resolving them. Transdisciplinary multiperspective integrative view on psychiatry (TIP) involving creative, person-centered narrative psychopharmacotherapy (CP-CNP) and empathization-based cognitive-emotional-behavioral therapy (EB-CEBT) as an art and practice of the empathic learning organization are presented in this article as well as the culture of empathy as a cornerstone of good clinical practice and mental health. We should all be driven by the vision of psychiatry and other mental health disciplines for a better world and promotion of mental health for all as a part of the Global Enlightenment 2.0 for Sustainability of the Earth System.

Background: The American psychiatrist Loren R. Mosher has passed to posterity as an eager proponent of a psychosocial approach to psychosis. The best example of this is the Soteria project that he founded in San Jose, California, in the 1970s. The contribution of Alma Zito Menn, ACSW, also merits attention as project director of Soteria and for her links to the Mental Research Institute, Palo Alto. She was later replaced as program director by Voyce Hendrix, LCSW, when she turned to other preoccupations linked to the grant continuation of Soteria. Equally, the nonprofessional staff of the facility should receive appreciation., Aim/objective: Bearing this in mind, the main aim of this paper is to reflect upon the Soteria project, giving voice to Mosher himself, while simultaneously connecting his ideas with other empirical works that have been published on this topic in recent decades., Methods: Using a selection from the extant literature assessing the implementation and outcomes of adapting Soteria-elements to different settings, I present here provisional results obtained from current research. First, I expound what Mosher hoped to achieve in creating Soteria and why it worked. In this respect, I go beyond what is commonly reported in scholarly works, where the Soteria project is considered without paying too great attention to its main architect, as if the project could be separated from the man who created it., Results and Conclusions: As I have corroborated here, there is today growing and promising scientific evidence validating the principles of the Soteria project. Undoubtedly, this would not have been possible without the pioneering work of Mosher, who, imbued with the tenets of interpersonal phenomenology, shook the psychiatric establishment, leading others to follow the path that he had begun.

The ability of Large Language Models (LLMs) to analyze and respond to freely written text is causing increasing excitement in the field of psychiatry; the application of such models presents unique opportunities and challenges for psychiatric applications. This review article seeks to offer a comprehensive overview of LLMs in psychiatry, their model architecture, potential use cases, and clinical considerations. LLM frameworks such as ChatGPT/GPT-4 are trained on huge amounts of text data that are sometimes fine-tuned for specific tasks. This opens up a wide range of possible psychiatric applications, such as accurately predicting individual patient risk factors for specific disorders, engaging in therapeutic intervention, and analyzing therapeutic material, to name a few. However, adoption in the psychiatric setting presents many challenges, including inherent limitations and biases in LLMs, concerns about explainability and privacy, and the potential damage resulting from produced misinformation. This review covers potential opportunities and limitations and highlights potential considerations when these models are applied in a real-world psychiatric context., Competing Interests: Declaration of competing interest AM-L has received consultant fees from: Agence Nationale de la Recherche, Brain Mind Institute, Brainsway, CISSN (Catania International Summer School of Neuroscience), Daimler und Benz Stiftung, Fondation FondaMental, Hector Stiftung II, Janssen-Cilag GmbH, Lundbeck A/S, Lundbeckfonden, Lundbeck Int. Neuroscience Foundation, MedinCell, Sage Therapeutics, Techspert.io, The LOOP Zürich, University Medical Center Utrecht, von Behring Röntgen Stiftung. AM-L has received speaker fees from: Arztekammer Nordrhein, BAG Psychiatrie Oberbayern, Biotest¨ AG, Forum Werkstatt Karlsruhe, International Society of Psychiatric Genetics, Brentwood, Klinik für Psychiatrie und Psychotherapie Ingolstadt, Lundbeck SAS France, med Update GmbH, Merz-Stiftung, Siemens Healthineers, Society of Biological Psychiatry. AM-L has received editorial fees from: American Association for the Advancement of Science, Elsevier, Thieme Verlag. ES received speaker fees from bfd buchholz-fachingormationsdienst GmbH and editorial fees from Lundbeckfonden. SV has no conflicts of interest to report., (Copyright © 2024. Published by Elsevier B.V.)

The explosion of generative AI offers promise for neuroimaging biomarker development in psychiatry, but effective adoption of AI methods requires clarity with respect to specific applications and challenges. These center on dataset sizes required to robustly train AI models along with feature selection that capture neural signals relevant to symptom and treatment targets. Here we discuss areas where generative AI could improve quantification of robust and reproducible brain-to-symptom associations to inform precision psychiatry applications, especially in the context of drug discovery. Finally, this communication discusses some challenges that need solutions for generative AI models to advance neuroimaging biomarkers in psychiatry., Competing Interests: Declaration of competing interest The views and opinions expressed in this manuscript are those of the authors only and do not necessarily represent the views, official policy or position of the U.S. Department of Health and Human Services or any of its affiliated institutions or agencies., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In the last decade, no other branch of clinical pharmacology has been subject to as much criticism of failed innovation and unsatisfactory effectiveness as psychopharmacology. Evolutionary psychiatry can offer original insights on the problems that complicate pharmacological research. Considering that invalid phenotyping is a major obstacle to drug development, an evolutionary perspective suggests targeting clinical phenotypes related to evolved behavior systems because they are more likely to map onto the underlying biology than constructs based on predetermined diagnostic criteria. Because of their emphasis on symptom remission, pharmacological studies of psychiatric populations rarely include functional capacities as the primary outcome measure and neglect the impact of social context on the effects of psychiatric drugs. Evolutionary psychiatry explains why it is appropriate to replace symptoms with functional capacities as the primary target of psychiatric therapies and why social context should be a major focus of studies assessing the effectiveness of drugs currently used and new drugs under development. When the focus of research shifts to those questions that go beyond the "disease-based" concept of drug action, evolutionary psychiatry clearly emerges as a reference framework to assess drug effectiveness and to optimize clinicians' decisions about prescribing, deprescribing, and non-prescribing., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Consultation liaison psychiatrists are frequently asked to evaluate patients with altered mental status (AMS). Psychiatrists have unique perspectives and approaches to care for confused patients, particularly optimizing facilitation of care and maintaining vigilance for diagnostic overshadowing. Psychiatrists also offer expertise in primary psychiatric illnesses that can overlap with AMS, and the most common etiology of AMS is delirium. In this article, we provide a consultation liaison psychiatrist perspective on AMS and related psychiatric conditions in addition to delirium. Manic and psychotic episodes have primary and secondary etiologies, with some symptoms that can overlap with delirium. Catatonia, neuroleptic malignant syndrome, and serotonin syndrome are potentially fatal emergencies, and require prompt index of suspicion to optimize clinical outcomes. Trauma sequelae, functional neurologic disorders, and dissociative disorders can present as puzzling cases that require psychiatric facilitation of care. Additionally, AMS is sometimes due to substance intoxication and withdrawal in the hospital. A nonstigmatizing approach to evaluation and management of delirium and AMS can ensure optimal patient care experiences and outcomes., Competing Interests: None declared., (Thieme. All rights reserved.)

Psychiatric disorders have a complex biological underpinning likely involving an interplay of genetic and environmental risk contributions. Substantial efforts are being made to use artificial intelligence approaches to integrate features within and across data types to increase our etiological understanding and advance personalized psychiatry. Network science offers a conceptual framework for exploring the often complex relationships across different levels of biological organization, from cellular mechanistic to brain-functional and phenotypic networks. Utilizing such network information effectively as part of artificial intelligence approaches is a promising route toward a more in-depth understanding of illness biology, the deciphering of patient heterogeneity, and the identification of signatures that may be sufficiently predictive to be clinically useful. Here, we present examples of how network information has been used as part of artificial intelligence within psychiatry and beyond and outline future perspectives on how personalized psychiatry approaches may profit from a closer integration of psychiatric research, artificial intelligence development, and network science., (© 2024 The Author(s). American Journal of Medical Genetics Part B: Neuropsychiatric Genetics published by Wiley Periodicals LLC.)

The paper further explores the development of psychotherapy in Polish psychiatry in the interwar period. Jaroszyński attempted to sketch out the idea of "emotional psychotherapy". Stryjeński organized a counseling clinic for the mentally ill, using psychotherapy as one of the means of treatment. Bilikiewicz developed oneiroanalysis - a psychotherapeutic method of dream analysis based on modifications of psychoanalysis. Gottliebowa advocated for the use of psychoanalytically influenced psychotherapy in the gynaecologist practice. Markuszewicz considered psychoanalysis the only psychotherapeutic modality aimed at unearthing the real causes of mental illnesses. Henryk Higier proposed to consider psychoanalysis practically as a method of psychotherapy and saw its heterogeneity as its advantage. Critical views on psychoanalysis as a psychotherapeutic method were delivered by Wirszubski and Mikulski. In general, psychotherapy in Polish psychiatry of the interwar period was highly influenced by psychoanalysis. Moreover, the understanding and practice of psychotherapy in public psychiatric facilities differed from that in private practice. In public psychiatric facilities, it was used mainly to deal with psychoses, so it urged clinicians to modify the classic psychoanalytic approach. In private practice, psychiatrists were dealing mainly with cases of neuroses and therefore could apply standard psychoanalytic procedures. Methods of suggestion, persuasion and hypnosis, characteristic of nineteenth-century psychotherapy, were still in use in Polish psychiatry of the interwar period. The main obstacles to the development of Polish psychotherapy in the interwar period were antisemitic attitudes contributing to hostility towards psychoanalysis, as well as the biological orientation of the majority of the Polish psychiatric society.

The paper explores the development of psychotherapy in Polish psychiatry in the interwar period from the perspective of Kurt Danziger's historical psychology. Firstly, the organizational and social context of the development of Polish psychiatric care in the interwar period and its impact on the development of psychotherapy is outlined. Then, the most influential in Poland, European psychotherapeutic developments in the interwar period are reconstructed. Finally, the views of psychiatric personnel of three psychiatric facilities proposing psychotherapy as one of the main means of treatment of mentally ill patients are introduced: Dziekanka Psychiatric Hospital, the Jewish Hospital in Warsaw and Psychiatry and Neuropathology Clinic of the Jagiellonian University in Krakow. Psychiatric personnel from Dziekanka Psychiatric Hospital, directed by Aleksander Piotrowski, understood psychotherapy broadly, as the influence of the environment on the patient's psyche. Psychiatrists of the Jewish Hospital in Warsaw, Adam Wizel, Gustaw Bychowski, Władysław Matecki, and Maurycy Bornsztajn developed psychoanalytically influenced psychotherapy of schizophrenia. Bychowski also advocated for the application of psychotherapy in such neglected groups of patients as children and the intellectually disabled. Jan Piltz and Eugeniusz Artwiński pursued psychotherapy in the treatment of war neuroses at the Psychiatry and Neuropathology Clinic of the Jagiellonian University.

Some models for mental disorders or behaviors (eg, suicide) have been successfully developed, allowing predictions at the population level. However, current demographic and clinical variables are neither sensitive nor specific enough for making individual actionable clinical predictions. A major hope of the "Decade of the Brain" was that biological measures (biomarkers) would solve these issues and lead to precision psychiatry. However, as models are based on sociodemographic and clinical data, even when these biomarkers differ significantly between groups of patients and control participants, they are still neither sensitive nor specific enough to be applied to individual patients. Technological advances over the past decade offer a promising approach based on new measures that may be essential for understanding mental disorders and predicting their trajectories. Several new tools allow us to continuously monitor objective behavioral measures (eg, hours of sleep) and densely sample subjective measures (eg, mood). The promise of this approach, referred to as digital phenotyping, was recognized almost a decade ago, with its potential impact on psychiatry being compared to the impact of the microscope on biological sciences. However, despite the intuitive belief that collecting densely sampled data (big data) improves clinical outcomes, recent clinical trials have not shown that incorporating digital phenotyping improves clinical outcomes. This viewpoint provides a stepwise development and implementation approach, similar to the one that has been successful in the prediction and prevention of cardiovascular disease, to achieve clinically actionable predictions in psychiatry., (©Abigail Ortiz, Benoit H Mulsant. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.08.2024.)

There is a substantial use of Complementary and Alternative Medicine (CAM) among both the general population and psychiatric patients, with only a minority of these users disclosing this information to their healthcare providers, including physicians and psychiatrists. This widespread use of CAM can impact positively or negatively on the clinical outcomes of psychiatric patients, and it is often done along with conventional medicines. Among CAM, phytotherapy has a major clinical relevance due to the introduction of potential adverse effects and drug interactions. Thus, the psychiatrist must learn about phytotherapy and stay up-to-date with solid scientific knowledge about phytotherapeutics/herbal medicines to ensure optimal outcomes for their patients. Furthermore, questions about herbal medicines should be routinely asked to psychiatric patients. Finally, scientifically sound research must be conducted on this subject.

The recent introduction of new-generation immunoassay methods allows the reliable quantification of structural brain markers in peripheral matrices. Neurofilament light chain (NfL), a neuron-specific cytoskeletal component released in extracellular matrices after neuroaxonal impairment, is considered a promising blood marker of active brain pathology. Given its sensitivity to a wide range of neuropathological alterations, NfL has been suggested for the use in clinical practice as a highly sensitive, but unspecific tool to quantify active brain pathology. While large efforts have been put in characterizing its clinical profile in many neurological conditions, NfL has received far less attention as a potential biomarker in major psychiatric disorders. Therefore, we briefly introduce NfL as a marker of neuroaxonal injury, systematically review recent findings on cerebrospinal fluid and blood NfL levels in patients with primary psychiatric conditions and highlight the opportunities and pitfalls. Current evidence suggests an elevation of blood NfL levels in patients with major depression, bipolar disorder, psychotic disorders, anorexia nervosa, and substance use disorders compared to physiological states. However, blood NfL levels strongly vary across diagnostic entities, clinical stage, and patient subgroups, and are influenced by several demographic, clinical, and analytical factors, which require accurate characterization. Potential clinical applications of NfL measure in psychiatry are seen in diagnostic and prognostic algorithms, to exclude neurodegenerative disease, in the assessment of brain toxicity for different pharmacological compounds, and in the longitudinal monitoring of treatment response. The high inter-individual variability of NfL levels and the lack of neurobiological understanding of its release are some of the main current limitations. Overall, this primer aims to introduce researchers and clinicians to NfL measure in the psychiatric field and to provide a conceptual framework for future research directions., (© 2024. The Author(s).)

Major depressive disorder (MDD) is a heterogeneous syndrome, associated with different levels of severity and impairment on the personal functioning for each patient. Classification systems in psychiatry, including ICD-11 and DSM-5, are used by clinicians in order to simplify the complexity of clinical manifestations. In particular, the DSM-5 introduced specifiers, subtypes, severity ratings, and cross-cutting symptom assessments allowing clinicians to better describe the specific clinical features of each patient. However, the use of DSM-5 specifiers for major depressive disorder in ordinary clinical practice is quite heterogeneous. The present study, using a Delphi method, aims to evaluate the consensus of a representative group of expert psychiatrists on a series of statements regarding the clinical utility and relevance of DSM-5 specifiers for major depressive disorder in ordinary clinical practice. Experts reached an almost perfect agreement on statements related to the use and clinical utility of DSM-5 specifiers in ordinary clinical practice. In particular, a complete consensus was found regarding the clinical utility for ordinary clinical practice of using DSM-5 specifiers. The use of specifiers is considered a first step toward a "dimensional" approach to the diagnosis of mental disorders., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

Background: One of the criticisms of the operational/diagnostic criteria, generalised since DSM-III, has been that they were shaped solely to achieve the best inter-peer reliability with no considerations for validity. This does not fully reflect reality since throughout the development of the criteria, there was an effort to define and fulfil some validity requirements. However, despite several attempts to create alternative diagnostic systems, there is still a widespread misunderstanding of the epistemological foundations that support this paradigm., Methods: In this article, we intend to analyse the epistemological context in which the operational criteria (OC) emerged and some of the validation processes they have undergone since their conception., Results: On the epistemological basis of these operational criteria (OC) the influence of Hempel has been widely discussed. However, the group from St. Louis and, also the DSM-III editors, never openly acknowledged his role and his contribution and revealed other influences such as other medical specialties (that used and validated several OC in the diagnosis of their diseases). On the other hand, contrary to what has often been mentioned there has been a continuous attempt to validate the OC since their conception. In the implementation and development of the operational paradigm, a more instrumental trend was followed, focused on utility, but with successive attempts to achieve realistic validity by searching for biological or psychological causality. The methodologies were initially expert-driven and gradually more data-driven and included some variables external to the construct itself, such as familial aggregation, diagnostic consistency over time, prognostic and other psychometric measures., (© 2023 John Wiley & Sons Ltd.)

The perspective of personalized medicine for brain disorders requires efficient learning models for anatomical neuroimaging-based prediction of clinical conditions. There is now a consensus on the benefit of deep learning (DL) in addressing many medical imaging tasks, such as image segmentation. However, for single-subject prediction problems, recent studies yielded contradictory results when comparing DL with Standard Machine Learning (SML) on top of classical feature extraction. Most existing comparative studies were limited in predicting phenotypes of little clinical interest, such as sex and age, and using a single dataset. Moreover, they conducted a limited analysis of the employed image pre-processing and feature selection strategies. This paper extensively compares DL and SML prediction capacity on five multi-site problems, including three increasingly complex clinical applications in psychiatry namely schizophrenia, bipolar disorder, and Autism Spectrum Disorder (ASD) diagnosis. To compensate for the relative scarcity of neuroimaging data on these clinical datasets, we also evaluate three pre-training strategies for transfer learning from brain imaging of the general healthy population: self-supervised learning, generative modeling and supervised learning with age. Overall, we find similar performance between randomly initialized DL and SML for the three clinical tasks and a similar scaling trend for sex prediction. This was replicated on an external dataset. We also show highly correlated discriminative brain regions between DL and linear ML models in all problems. Nonetheless, we demonstrate that self-supervised pre-training on large-scale healthy population imaging datasets (N≈10k), along with Deep Ensemble, allows DL to learn robust and transferable representations to smaller-scale clinical datasets (N≤1k). It largely outperforms SML on 2 out of 3 clinical tasks both in internal and external test sets. These findings suggest that the improvement of DL over SML in anatomical neuroimaging mainly comes from its capacity to learn meaningful and useful abstract representations of the brain anatomy, and it sheds light on the potential of transfer learning for personalized medicine in psychiatry., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Telepsychiatry (TP), a live video meeting, has been implemented in many contexts and settings. It has a distinct advantage in the psychiatric emergency department (ED) setting, as it expedites expert assessments for psychiatric patients. However, limited knowledge exits for TP's effectiveness in the ED setting, as well as the process of implementing TP in this setting., Objective: This scoping review aimed to review the existing evidence for the administrative and clinical outcomes for TP in the ED setting and to identify the barriers and facilitators to implementing TP in this setting., Methods: The scoping review was conducted according to the guidelines for the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Three electronic databases were examined: PubMed, Embase, and Web of Science. The databases were searched from January 2013 to April 2023 for papers and their bibliography. A total of 2816 potentially relevant papers were retrieved from the initial search. Studies were screened and selected independently by 2 authors., Results: A total of 11 articles were included. Ten papers reported on administrative and clinical outcomes of TP use in the ED setting and 1 on the barriers and facilitators of its implementation. TP is used in urban and rural areas and for settings with and with no on-site psychiatric services. Evidence shows that TP reduced waiting time for psychiatric evaluation, but in some studies, it was associated with prolonged total length of stay in the ED compared with in-person evaluation. Findings indicate lower admission rates in patients assessed with TP in the ED. Limited data were reported for TP costs, its use for involuntary commitment evaluations, and its use for particular subgroups of patients (eg, those with a particular diagnosis). A single paper examined TP implementation process in the ED, which explored the barriers and facilitators for implementation among patients and staff in a rural setting., Conclusions: Based on the extant studies, TP seems to be generally feasible and acceptable to key stakeholders. However, this review detected a gap in the literature regarding TP's effectiveness and implementation process in the ED setting. Specific attention should be paid to the examination of this service for specific groups of patients, as well as its use to enable assessments for possible involuntary commitment., (©Ligat Shalev, Renana Eitan, Adam J Rose. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 15.07.2024.)

According to international experience, the conditions for the successful outcome of a psychiatric reform are the following: (a) Existence of political will (supporting a national plan with assessment, monitoring, and corrective intervention procedures for structural dysfunctions, etc.). (b) Strong mental health leadership (executive expertise and skills that advance the public health agenda). (c) Challenging the dominance of the biomedical model in therapeutic practice through the promotion of holistic care practices, evidence-based innovative actions, collaborative care, the promotion of recovery culture, and the and the use of innovative digital tools. (d) Ensuring necessary resources over time, so that resources from the transition of the asylum model to a model of sectorial community mental health services "follow" the patient. (e) Strengthening the participation of service recipients and their families in decision-making processes and evaluation of care quality. (f) Practices based on ethical principles (value-based practice) and not only on the always necessary documentation (evidence-based practice).1- 4 Convergent evidence from the "ex post" evaluation of the implementation of the national plan Psychargos 2000-20095 and from the recent rapid assessment of the psychiatric reform by the Ministry of Health and the WHO Athens office (SWOT analysis)6 indicates "serious fragmentation of services, an uncoordinated system that often results in inappropriate service provision, a lack of epidemiological studies and studies concerning the local needs of specific populations, uneven development of services between different regions of the country, a large number of specialized professionals with significant deficits in community psychiatry expertise, a lack of personnel in supportive roles, significant gaps in specialized services (for individuals with autism spectrum disorders, intellectual disabilities, eating disorders, old and new addictions, and community forensic psychiatry services)". We would also like to highlight lack of coordination and collaboration among different mental health service systems (public primary and secondary service providers, NGOs, municipal services, mental health services of the armed forces, private sector), complete absence of systematic evaluation and monitoring (lack of quality of care indicators, clinical outcomes, epidemiological profile of each service), lack of quality assurance mechanisms and clinical management systems, insufficient number of beds mainly for acute cases, unclear protocols for discharge issuance and ensuring continuity of care, deficient budget for Mental Health in relation to the overall healthcare expenditure (currently 3.3%), and finally, one of the highest rates of involuntary hospitalizations in Europe, which is linked to serious issues concerning the protection of the rights of service users. After the pandemic and the emergence of the silent but expected mental health pandemic, WHO, EU, and the Greek Ministry of Health emphasized the need to adopt a public mental health agenda with an emphasis on community psychiatry in order to address both the old structural dysfunctions and inadequacies of psychiatric reform (regulation 815/1984, Leros I-Leros II plan, Psychargos A & B, incomplete implementation of laws 2071/1992 & 2716/1999, incomplete deinstitutionalization of the remaining psychiatric hospitals). However, it is time to reflect that it is not possible to talk today about the need to update and implement a new national plan to upgrade mental health in the country without answering basic questions, both old and new, about the wider context of its implementation. The transformation of the deficient psychiatric care in the country cannot be completed without the urgent restructuring of the National Health System7 and the reform of the Greek welfare state itself, which is also characterized by irrationality, inequalities, bureaucratic inefficiency, and fragmentation.8 As we should have learned from the bankruptcy and the prolonged economic, social, and cultural crisis in our country, reforms usually pay off in the long term, while the time horizon of the applied policies is narrow and usually reaching the next election. The fact is that in any reform effort, including psychiatry, the political system does not demonstrate the ability to promote transparency, evaluation, stable rules of regulation, reference to a universally applicable legal and institutional framework, the limitation of clientelism and guild resistances. From this point of view, it is necessary to give meaning in the context of Greek psychiatric reform to the professional burnout of the National Health System workers, the lack of motivation and vision, the intrusion into the NGO space by new entities without any connection to the culture of psychiatry reform, the guild resistances of all relevant specialties, the selective use of psychotherapeutic techniques, as trends of discrediting the relief of social and psychological suffering in the field of public mental health. There is an urgent need to understand new pathologies (narcissistic disorders, new forms of addiction, eating disorders, "pathology of emptiness", adolescent delinquency and suicide, psychosomatic manifestations due to high stress, pathology of fluid social ties, deficient socialization of young people "outside of their algorithms") through a solid and coherent analysis of the toxic postmodernity culture. In addition to the social determinants of mental health,9 it is necessary in clinical work to also assess the psychological factors, such as uncertainty, conflict, loss of control, and incomplete information, that burden human health.10 In order to reduce the gap between declarations and real life, there is an urgent need to overcome the blind spots of psychiatric reform in the country by establishing internal and external evaluation processes, training young professionals in holistic care and community networking and communication skills, retraining leaders for organizational change, and strengthening the participation of service users in the context of deepening democracy in mental health. As mental health professionals, the object of our work in the community should be the reconstruction of meaning and the fragile or non-existent social bond in subjects who have been cut off from any possible production of meaning and participation in their history. Why should our therapeutic responses be stereotypically repetitive in the face of these complex, radical changes in the meta-context and the new demands of our patients? After all, as the philosopher Ernst Bloch puts it, utopia is "that which does not exist yet.".

Objectives: After more than 20 years of work, the World Health Organization's efforts have culminated in the adoption of the 11th revision of the International Classification of Diseases (ICD-11). The process has been guided by the principles of global applicability, scientific validity, and clinical utility. The update of the chapter on mental, behavioral, and neurodevelopmental disorders (MBND) within the ICD-11 has generated widespread interest worldwide. This has raised various questions about the status of mental disorder nosology, changes to diagnostic guidelines, and the potential implications for clinical practice., Methods: We conducted a narrative analysis of the literature in four different languages to track the various stages of the ICD-11 revision and to highlight the major changes. We searched databases such as PubMed, EMBASE, MEDLINE, and Google Scholar, and consulted the official websites of the WHO, APA, and UNICEF. A total of 79 articles from 40 different editorials and websites were analyzed and included in this study., Results: The new chapters on mental disorders in the ICD-11 include 21 groups, as opposed to the 11 in the ICD-10. The changes aim to align the diagnoses with those of the DSM-5 and introduce a new chapter structure, new diagnostic categories, modifications to diagnostic criteria, and advancements in dimensionality. For the first time in the history of the ICD, sleep and wakefulness disorders, as well as disorders related to sexual health, have been addressed in separate chapters of the international classification. Four new diagnoses have been added: complex post-traumatic stress disorder (PTSD), gaming disorder, prolonged grief disorder, and compulsive sexual behavior which replaces "excessive sexual activity" in the ICD-10. Moreover, the ICD-11 revision has brought about a fundamental change in the clinical conceptualization of addictive behaviors, introducing a distinction between substance use disorders and addictive behaviors. The criteria for many existing conditions have been revised, particularly those related to bipolar disorders, eating disorders, and gender identity disorders., Conclusions: The revision process for mental, behavioral, and neurodevelopmental disorders in the ICD-11 has witnessed unprecedented participation in the history of mental disorder classification. These changes could have a significant impact on clinical practice in psychiatry. However, it is crucial to examine the advantages and limitations of this new classification compared to previous versions., (Copyright © 2023 L'Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.)