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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.)

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.)

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.)

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.)

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: 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.)

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.)

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.

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.".

Objective: To identify the perceptions of the patients who received alternative care by telepsychiatry at the Cayetano Heredia Hospital (HCH). Methods: This research consisted of two phases: (1) transcultural adaptation of the Telehealth Usability Questionnaire (TUQ) with three experts and (2) application of the questionnaire in 183 patients from psychiatry in HCH. Nonparametric tests were used to determine the association between variables. Results: We applied 20 questions to 60 men and 123 women, with a median age of 45. The ease of using the virtual consultation service, the comfort with its use, and the general satisfaction had a score of 6 out of 7 and are associated with the number of devices that patients have and their degree of education. The usefulness, communication by virtual means, and the solution of technical problems had scores higher than 6, being considered acceptable by the patients. Willingness to have a teleconsultation again was high and was associated with patient satisfaction with the consultation. Conclusions: Most patients were satisfied with telepsychiatry via telemonitoring during the COVID-19 pandemic. The usage of validated tools such as TUQ might be included as part of evaluations of new telemedicine services .

Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring., Competing Interests: Declaration of competing interest Authors have no competing interests to declare., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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.)

Precision psychiatry is an emerging field that aims to provide individualized approaches to mental health care. An important strategy to achieve this precision is to reduce uncertainty about prognosis and treatment response. Multivariate analysis and machine learning are used to create outcome prediction models based on clinical data such as demographics, symptom assessments, genetic information, and brain imaging. While much emphasis has been placed on technical innovation, the complex and varied nature of mental health presents significant challenges to the successful implementation of these models. From this perspective, I review ten challenges in the field of precision psychiatry, including the need for studies on real-world populations and realistic clinical outcome definitions, and consideration of treatment-related factors such as placebo effects and non-adherence to prescriptions. Fairness, prospective validation in comparison to current practice and implementation studies of prediction models are other key issues that are currently understudied. A shift is proposed from retrospective studies based on linear and static concepts of disease towards prospective research that considers the importance of contextual factors and the dynamic and complex nature of mental health.

Aims: To investigate the negative attitudes of Japanese psychiatrists toward atypical long-acting injectable (LAI) antipsychotics, which are the current mainstream LAIs in Japan., Methods: We surveyed 69 Japanese psychiatrists using a 5-point Likert scale to assess their attitudes toward atypical LAI antipsychotics. Our assessment referenced concerns identified in a study conducted in Japan a decade ago, which found significant differences when compared with a survey of German psychiatrists. We also identified the factors influencing these negative attitudes. Additionally, the results were compared with those of previous Japanese and German studies., Results: More than 50% of Japanese psychiatrists expressed negative attitudes toward atypical LAI antipsychotics in various areas. These concerns included apprehensions about cost, reluctance to recommend them initially, pain from injections, complexity of switching to LAI, usage in first-episode cases, and sufficient medication adherence with oral drugs. In all three studies, cost and adequate adherence to oral medication were concerns that exceeded the average of the three negative comments. Age and experience in psychiatry influenced the psychiatrists' attitudes toward using these drugs in first-episode cases., Conclusions: These findings shed light on the reasons for the underutilization of atypical LAI antipsychotics and suggest opportunities to enhance their appropriate use in clinical settings., (© 2024 The Authors. Neuropsychopharmacology Reports published by John Wiley & Sons Australia, Ltd on behalf of The Japanese Society of Neuropsychopharmacology.)

Psychiatrists play a crucial role in evaluating requests and treatment indications for individuals experiencing gender incongruence, while also providing support throughout the transition process. Their work involves addressing both the psychological and somatic aspects of this journey, facilitating the profound identity changes it entails., Competing Interests: Les auteurs n’ont déclaré aucun conflit d’intérêts en relation avec cet article.

Background: This document represents an updated collaboration between the American Psychiatric Association (APA) and the American Telemedicine Association (ATA) to create a consolidated update of the previous APA and ATA official documents and resources in telemental health, to provide a single guide on clinical best practices for providing mental health services through synchronous videoconference. Methods: A joint writing committee drawn from the APA Committee on Telepsychiatry and the ATA TMH Special Interest Group (TMH SIG). was convened to draft and finalize the guidelines. This document draws directly from the 2018 APA/ATA guide and the ATA s previous guidelines, selecting from key statements/guidelines, consolidating them across documents, and then updating them where indicated. Guideline approval was provided following internal review by the APA, the ATA, the Board of Directors of the ATA, and the Joint Reference Committee of the APA. Results: The guidelines contain requirements, recommendations, and actions that are identified by text containing the keywords "shall," "should," or "may." Conclusions: Compliance with these recommendations will not guarantee accurate diagnoses or successful outcomes. The purpose of this guide is to assist providers in providing effective and safe medical care founded on expert consensus, research evidence, available resources, and patient needs.

There is a growing focus on the computational aspects of psychiatric disorders in humans. This idea also is gaining traction in nonhuman animal studies. Commenting on a new comprehensive overview of the benefits of applying this approach in translational research by Neville et al. (Cognitive Affective & Behavioral Neuroscience 1-14, 2024), we discuss the implications for translational model validity within this framework. We argue that thinking computationally in translational psychiatry calls for a change in the way that we evaluate animal models of human psychiatric processes, with a shift in focus towards symptom-producing computations rather than the symptoms themselves. Further, in line with Neville et al.'s adoption of the reinforcement learning framework to model animal behaviour, we illustrate how this approach can be applied beyond simple decision-making paradigms to model more naturalistic behaviours., (© 2024. The Author(s).)

The routine in-depth characterization of patients with methods of clinical and scale-based examination, neuropsychology, based on biomaterials, and sensor-based information opens up transformative possibilities on the way to personalized diagnostics, treatment and prevention in psychiatry, psychotherapy, and psychosomatics. Effective integration of the additional temporal and logistical effort into everyday care as well as the acceptance by patients are critical to the success of such an approach but there is little evidence on this to date. We report here on the establishment of the Diagnosis and Admission Center (DAZ) at the Central Institute of Mental Health (ZI) in Mannheim. The DAZ is an outpatient unit upstream of other care structures for clinical and scientific phenotyping across diagnoses as a starting point for data-driven, individualized pathways to further treatment, diagnostics or research. We describe the functions, goals, and implementation of the newly created clinical scientific translational structure, provide an overview of the patient populations it has reached, and provide data on its acceptance. In this context, the close integration with downstream clinical processes enables a better coordinated and demand-oriented allocation. In addition, DAZ enables a faster start of disorder-specific diagnostics and treatment. Since its launch in April 2021 up to the end of 2022, 1021 patients underwent psychiatric evaluation at DAZ during a pilot phase. The patient sample corresponded to a representative sample from standard care and the newly established processes were regarded as helpful by patients. In summary, the DAZ uniquely combines the interests and needs of patient with the collection of scientifically relevant data., (© 2024. The Author(s).)

Although neuroimaging has been widely applied in psychiatry, much of the exuberance in decades past has been tempered by failed replications and a lack of definitive evidence to support the utility of imaging to inform clinical decisions. There are multiple promising ways forward to demonstrate the relevance of neuroimaging for psychiatry at the individual patient level. Ultra-high field magnetic resonance imaging is developing as a sensitive measure of neurometabolic processes of particular relevance that holds promise as a new way to characterize patient abnormalities as well as variability in response to treatment. Neuroimaging may also be particularly suited to the science of brain stimulation interventions in psychiatry given that imaging can both inform brain targeting as well as measure changes in brain circuit communication as a function of how effectively interventions improve symptoms. We argue that a greater focus on individual patient imaging data will pave the way to stronger relevance to clinical care in psychiatry. We also stress the importance of using imaging in symptom-relevant experimental manipulations and how relevance will be best demonstrated by pairing imaging with differential treatment prediction and outcome measurement. The priorities for using brain imaging to inform psychiatry may be shifting, which compels the field to solidify clinical relevance for individual patients over exploratory associations and biomarkers that ultimately fail to replicate., (© 2024. The Author(s).)

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: Psychiatry may currently hold unprecedented knowledge in the diagnosis and treatment of psychiatric conditions. Yet, there is a widely held belief that this knowledge is not adequately integrated, nor does it fully account for the complexity of the phenomena under study., Objective: To assess the effectiveness of a system-oriented and network-focused approach in capturing and integrating the complexity of psychiatric disorders. Next, to explore the epistemological implications of such an approach., Method: Narrative literature review., Results: Psychiatric research is still too often characterized by reductionism, linear-causal pathogenesis, and traditional nosology. There appears to be a need for a different metatheoretical model in psychiatry., Conclusion: The development from General System Theory to complex dynamic systems thinking and network theory holds significant epistemological implications for the future of the field, how we conduct science, and the way we frame and structure our care systems.

Presently, there is an increased interest in expanding the range of diagnostic and scientific applications of electroencephalography (EEG). The method is attractive due to non-invasiveness, availability of equipment with a wide range of modifications for various purposes, and the ability to track the dynamics of brain electrical activity directly and with high temporal resolution. Spectral, coherency and other types of analysis provide volumetric information about its power, frequency distribution, spatial organization of signal and its self-similarity in dynamics or in different sections at a time. The development of computing technologies provides processing of volumetric data obtained using EEG and a qualitatively new level of their analysis using various mathematical models. This review discusses benefits and limitations of using the EEG in scientific research, currently known interpretation of the obtained data and its physiological and pathological correlates. It is expected to determine the complex relationship between the parameters of brain electrical activity and various functional and pathological conditions. The possibility of using EEG characteristics as biomarkers of various physiological and pathological conditions is being considered. Electronic databases, including MEDLINE (on PubMed), Google Scholar and Russian Scientific Citation Index (RSCI, on elibrary.ru), scientific journals and books were searched to find relevant studies.

Technologies in the 21st century provide increasingly detailed and accurate maps of brain structure and function. So why don't psychiatrists order brain imaging on all our patients? Here we briefly review major neuroimaging methods and some of their findings in psychiatry. As clinicians and neuroimaging researchers, we are eager to bring brain imaging into daily clinical practice. However, to be clinically useful, any test in medicine must demonstrate adequate test statistics, and show proven benefits that outweigh its risks and costs. In 2024, beyond certain limited circumstances, we have no imaging tests that can meet those standards to provide diagnosis or guide treatment. This cold fact explains why for most psychiatric patients, neuroimaging is not currently recommended by professional organizations or the National Institute of Mental Health., Competing Interests: Disclosure: As of 2020, KJB received honorarium from: SK Life Science, Inc./scientific advisory board; Medscape/CME program on PD psychosis; Mededicus/CME program on PD psychosis; Informa Pharma Intelligence; and Putnam Associates as a consultant. Artificial intelligence was not used in the study, research, preparation, or writing of this manuscript., (Copyright 2024 by the Missouri State Medical Association.)

Objective: To assess the extent to which articles examining telepsychiatry after the start of the COVID-19 pandemic provided racial and sociodemographic characteristics for people receiving audiovisual (video) versus audio-only telepsychiatry., Data Sources, Study Selection, and Data Extraction: We employed the keyword telepsychiatry and screened all peer-reviewed articles in PubMed published from March 1, 2020, until November 23, 2022, prior to the federal government's announcement of the impending end to the COVID-19 public health emergency. We retrieved and reviewed the full-text articles of 553 results for potential inclusion, of which 266 were original research articles., Results: We found that 106 of 553 articles had any mention of differences between audio-only and audiovisual telepsychiatry. Twenty-nine of 553 articles described potential socioeconomic differences in the distribution of people receiving audio-only versus audiovisual telepsychiatry, and 20 of 553 described potential racial/ethnic differences. Among research articles, most (213/266) did not differentiate between videoconferencing and audio-only/telephone-based telehealth services. A total of 4 research articles provided racial and sociodemographic characteristics of individuals who received audio-only versus audiovisual telepsychiatry services during the COVID-19 pandemic, all of which were conducted in relatively small regional samples that could not be generalized to the US as a whole., Conclusions: Overall, this analysis underscores that empirical data are lacking on racial and sociodemographic distribution of audio-only versus audiovisual telepsychiatry services since the COVID-19 pandemic., Prim Care Companion CNS Disord 2023;25(6):23r03563 ., Author affiliations are listed at the end of this article., (© Copyright 2023 Physicians Postgraduate Press, Inc.)

Aim: Recent advances in natural language processing models are expected to provide diagnostic assistance in psychiatry from the history of present illness (HPI). However, existing studies have been limited, with the target diseases including only major diseases, small sample sizes, or no comparison with diagnoses made by psychiatrists to ensure accuracy. Therefore, we formulated an accurate diagnostic model that covers all psychiatric disorders., Methods: HPIs and diagnoses were extracted from discharge summaries of 2,642 cases at the Nara Medical University Hospital, Japan, from 21 May 2007, to 31 May 31 2021. The diagnoses were classified into 11 classes according to the code from ICD-10 Chapter V. Using UTH-BERT pre-trained on the electronic medical records of the University of Tokyo Hospital, Japan, we predicted the main diagnoses at discharge based on HPIs and compared the concordance rate with the results of psychiatrists. The psychiatrists were divided into two groups: semi-Designated with 3-4 years of experience and Residents with only 2 months of experience., Results: The model's match rate was 74.3%, compared to 71.5% for the semi-Designated psychiatrists and 69.4% for the Residents. If the cases were limited to those correctly answered by the semi-Designated group, the model and the Residents performed at 84.9% and 83.3%, respectively., Conclusion: We demonstrated that the model matched the diagnosis predicted from the HPI with a high probability to the principal diagnosis at discharge. Hence, the model can provide diagnostic suggestions in actual clinical practice., (© 2023 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.)

Background: Alcohol use disorder is insufficiently treated because it is poorly detected. Improved screening in general hospitals is a major issue in treatment organisation., Subject and Method: Through a retrospective exploratory study based on a review of medical records over four years (2016-19) before the COVID pandemic, we recorded the number of liaison psychiatric referrals for alcohol use disorder and identified two indicators: follow-up care implemented and survival at three years., Results: Subject to the limitations of a retrospective review of medical records, we observed that 136 of 859 referrals concerned an alcohol use disorder. At three years, 25% (34 patients) had died. The causes of death were analysed, which revealed that 65% were directly attributable to alcohol consumption. Ongoing follow-up care was documented for only 17% (15 patients) of the 136 referred patients. Twelve patients who were followed up (80%) were in remission at one year., Conclusion: Although the results showed the efficacy of psychiatric follow-up care, the low recruitment of patients receiving regular follow-up care confirmed the problem of access to treatment. Our results support the need for screening and earlier intervention in general hospitals by raising awareness and training all doctors, but also by reflecting on new methods of liaison psychiatry treatment.

Purpose of Review: The purpose of this review is to summarize factors contributing to the current state of pediatric mental health access in ED settings and synthesize the existing literature on the use of telepsychiatry to extend access to care, with particular focus on feasibility and sustainability., Recent Findings: Children are presenting to emergency departments (EDs) with mental health concerns at an increasing rate, while ED capacity to treat psychiatric needs in children remains insufficient. This growing problem is compounded by decreased access to outpatient care and inpatient psychiatric beds, resulting in exorbitantly long waiting times, or "boarding," of children in crisis. Telepsychiatry has emerged as a strategy to decrease boarding of pediatric patients in ED settings by utilizing remote psychiatric professionals to provide consultation and assessment. Telepsychiatry in ED settings is an effective strategy to increase access to care and decrease length of stay for pediatric patients., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Introduction: Telepsychiatry consultation for rural providers may help address local staffing needs while ensuring timely and appropriate care from behavioral health experts. The purpose of this study was to assess the implementation of a telepsychiatry consultation service within medical and psychiatry inpatient units of hospitals serving predominantly rural areas. Methods: A mixed-methods study with qualitative interviews of site personnel and quantitative assessment of electronic health record data was conducted across 6 facilities in 3 U.S. states between June 2019 and May 2021. We interviewed 15 health care professionals 6 months after telepsychiatry was implemented, and we identified emerging themes related to the inpatient telepsychiatry service implementation and utilization through an inductive qualitative analysis approach. We then applied the themes emerging from this study to existing implementation science theoretical frameworks. Results: Telepsychiatry consultation was utilized for 437 medical inpatient cases and 531 psychiatric inpatient units. Average encounters by site ranged from 1 to 20 per month. The three main domains from the qualitative assessment included the impact on the care process (the partnership between inpatient units and the telehealth hub, and logistical dynamics), the care provider (resource availability in inpatient units and changes in inpatient units' capability), and the patient (impact on patient safety and care). Discussion: Implementation of a telepsychiatry service in the inpatient setting holds the promise of being beneficial to the patient, local hospital, and the rural community. In this study, we found that implementing this telepsychiatry service improved the clinical care processes, while addressing both the providers' and patients' needs.

Consultation Liaison Psychiatry (CLP) deals with the interface shared between psychiatry and various other disciplines of medicine. The interface shared by psychiatry and ophthalmology is among the lesser discussed ones in the field of CLP, despite the fact that it holds clinical relevance in the evaluation, management and outcomes of both psychiatric and ophthalmological disorders. This narrative review focusses on the ophthalmological aspects of psychiatric disorders, with respect to their manifestations, assessment, and management. Psychiatric disorders, including schizophrenia, affective disorders, 'functional' disorders, and substance use disorders, have numerous ophthalmic manifestations, which can have clinical implications for the patients. Even the psychotropic drugs given for psychiatric disorders can lead to side effects affecting the eye, but these are among the lesser-discussed side effects. Some psychiatric disorders can be investigated using various ophthalmic functions, the assessments ranging from simple physical examination to the use of instruments like a fundoscope, which can be useful for a psychiatrist in their routine practice. Lastly, eye functions can also be used in the treatment of psychiatric conditions, as is seen in eye movement desensitization and reprocessing. This review reiterates the fact that more attention needs to be given to the field of 'psycho-ophthalmology', which holds great promise in the coming days.

Computational psychiatry is a field aimed at developing formal models of information processing in the human brain, and how alterations in this processing can lead to clinical phenomena. There has been significant progress in the development of tasks and how to model them, presenting an opportunity to incorporate computational psychiatry methodologies into large- scale research projects or into clinical practice. In this viewpoint, we explore some of the barriers to incorporation of computational psychiatry tasks and models into wider mainstream research directions. These barriers include the time required for participants to complete tasks, test-retest reliability, limited ecological validity, as well as practical concerns, such as lack of computational expertise and the expense and large sample sizes traditionally required to validate tasks and models. We then discuss solutions, such as the redesigning of tasks with a view toward feasibility, and the integration of tasks into more ecologically valid and standardized game platforms that can be more easily disseminated. Finally, we provide an example of how one task, the conditioned hallucinations task, might be translated into such a game. It is our hope that interest in the creation of more accessible and feasible computational tasks will help computational methods make more positive impacts on research as well as, eventually, clinical practice., (© 2023. The Author(s).)

Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo neuroimaging data have led to fundamental advances in our understanding of the neurobiology of psychiatric illnesses. More recently, access to computational resources and large, publicly available datasets alongside the rise of predictive modeling and precision medicine approaches have facilitated the study of psychiatric illnesses at an individual level. Data-driven machine learning analyses can be applied to identify disease-relevant biological subtypes, predict individual symptom profiles, and recommend personalized therapeutic interventions. However, when developing these predictive models, methodological choices must be carefully considered to ensure accurate, robust, and interpretable results. Choices pertaining to algorithms, neuroimaging modalities and states, data transformation, phenotypes, parcellations, sample sizes, and populations we are specifically studying can influence model performance. Here, we review applications of neuroimaging-based machine learning models to study psychiatric illnesses and discuss the effects of different methodological choices on model performance. An understanding of these effects is crucial for the proper implementation of predictive models in psychiatry and will facilitate more accurate diagnoses, prognoses, and therapeutics., (Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

The ability of our current psychiatric nosology to accurately delineate clinical populations and inform effective treatment plans has reached a critical point with only moderately successful interventions and high relapse rates. These challenges continue to motivate the search for approaches to better stratify clinical populations into more homogeneous delineations, to better inform diagnosis and disease evaluation, and prescribe and develop more precise treatment plans. The promise of brain-based subtyping based on neuroimaging data is that finding subgroups of individuals with a common biological signature will facilitate the development of biologically grounded, targeted treatments. This review provides a snapshot of the current state of the field in empirical brain-based subtyping studies in child, adolescent, and adult psychiatric populations published between 2019 and March 2022. We found that there is vast methodological exploration and a surprising number of new methods being created for the specific purpose of brain-based subtyping. However, this methodological exploration and advancement is not being met with rigorous validation approaches that assess both reproducibility and clinical utility of the discovered brain-based subtypes. We also found evidence for a collaboration crisis, in which methodological exploration and advancements are not clearly grounded in clinical goals. We propose several steps that we believe are crucial to address these shortcomings in the field. We conclude, and agree with the authors of the reviewed studies, that the discovery of biologically grounded subtypes would be a significant advancement for treatment development in psychiatry., (Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Computational psychiatry, a relatively new yet prolific field that aims to understand psychiatric disorders with formal theories about the brain, has seen tremendous growth in the past decade. Despite initial excitement, actual progress made by computational psychiatry seems stagnant. Meanwhile, understanding of the human brain has benefited tremendously from recent progress in intracranial neuroscience. Specifically, invasive techniques such as stereotactic electroencephalography, electrocorticography, and deep brain stimulation have provided a unique opportunity to precisely measure and causally modulate neurophysiological activity in the living human brain. In this review, we summarize progress and drawbacks in both computational psychiatry and invasive electrophysiology and propose that their combination presents a highly promising new direction-invasive computational psychiatry. The value of this approach is at least twofold. First, it advances our mechanistic understanding of the neural computations of mental states by providing a spatiotemporally precise depiction of neural activity that is traditionally unattainable using noninvasive techniques with human subjects. Second, it offers a direct and immediate way to modulate brain states through stimulation of algorithmically defined neural regions and circuits (i.e., algorithmic targeting), thus providing both causal and therapeutic insights. We then present depression as a use case where the combination of computational and invasive approaches has already shown initial success. We conclude by outlining future directions as a road map for this exciting new field as well as presenting cautions about issues such as ethical concerns and generalizability of findings., (Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Inpatient consultation-liaison (CL) psychiatry teams routinely facilitate the transfer of medically stable patients in behavioral health crisis from the general hospital to inpatient psychiatric units. The COVID-19 pandemic had a significant impact on this process when inpatient psychiatric units were unable to provide care for patients with asymptomatic COVID-19 infection because of infection control concerns in units unable to accommodate isolation precautions. Similar to other disrupted hospital workflows, these clinical handoffs became more complicated by requiring COVID exposed or COVID+ patients in the midst of behavioral health crisis to quarantine or isolate on general hospital units if not otherwise stable for discharge to the community. To better respond to the growing number of patients isolating in the general hospital during the 2022 Omicron surge, we used quality improvement (QI) methodology to illustrate the need to create a COVID+ unit in the inpatient psychiatric hospital to care for the growing cohort of COVID+ patients in psychiatric crisis who were otherwise unable to access traditional psychiatric hospital care because of their isolation status., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The physician's perspective on clinical assessment skills by nurses in psychiatry: A case study Abstract: Background: People with a mental and physical illness receive less good somatic care compared to the general population. Patients in psychiatry receive better medical care when nurses have in-depth medical expertise. Therefore, in a psychiatric institution in Switzerland in 2017, the Clinical Assessment and Decision Making (CADM) by nurses was implemented. Aim: The aim of the study was to investigate how physicians and senior psychologists experience the implementation of CADM by nurses in order to derive recommendations on how to optimize the collaboration and promote a sustainable establishment. Methods: An embedded single case study was used, and the data analysis was based on grounded theory strategies by Charmaz. Semi-structured expert interviews (n = 11) and open unstructured observations were conducted in a psychiatric institution in Switzerland. Results: The results showed nine relevant aspects regarding collaboration and CADM by nurses: "Strengths", "Weaknesses", "Risks", "Opportunities", "Expectations", "Challenges", "Benefits", "Competencies of CADM nurses" and "Wishes for the future". Conclusions: The physicians and senior psychologists considered the CADM by nurses as an added value for the interprofessional team and for the patients. Lack of clarity regarding the scope of responsibilities, role definitions and possible applications made the implementation of CADM difficult.

This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain-from cognitive and computational neuroscience-as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we-or our brains-encode uncertainty or its complement, precision. It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms., (© 2022. The Author(s).)

Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)