Phytotherapy in psychiatry: why psychiatrists should know about it.

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.

Cariprazine in Psychiatry: A Comprehensive Review of Efficacy, Safety, and Therapeutic Potential.

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.

Telepsychiatry in the Treatment of Major Depressive Disorders.

This chapter explores the transformative role of telepsychiatry in managing major depressive disorders (MDD). Traversing geographical barriers and reducing stigma, this innovative branch of telemedicine leverages digital platforms to deliver effective psychiatric care. We investigate the evolution of telepsychiatry, examining its diverse interventions such as videoconferencing-based psychotherapy, medication management, and mobile applications. While offering significant advantages like increased accessibility, cost-effectiveness, and improved patient engagement, challenges in telepsychiatry include technological barriers, privacy concerns, ethical and legal considerations, and digital literacy gaps. Looking forward, emerging technologies like virtual reality, artificial intelligence, and precision medicine hold immense potential to personalize and enhance treatment effectiveness. Recognizing its limitations and advocating for equitable access, this chapter underscores telepsychiatry's power to revolutionize MDD treatment, making quality mental healthcare a reality for all.

Diagnostic accuracy of large language models in psychiatry.

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.

[Complex dynamical systems in psychiatry: an integrative approach]. / Psychopathologie als zelforganisatie van complexe dynamische systemen.

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.

Beyond Step Count: Are We Ready to Use Digital Phenotyping to Make Actionable Individual Predictions in Psychiatry?

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.

Climate Change and Aging: Implications for Psychiatric Care.

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.

Drug development in psychopharmacology: Insights from evolutionary psychiatry.

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.

The Use of Telepsychiatry Services in Emergency Settings: Scoping Review.

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.

[Electroencephalography: features of the obtained data and its applicability in psychiatry]. / Elektroentsefalografiya: osobennosti poluchaemykh dannykh i ikh primenimost' v psikhiatrii.

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.

Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry.

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.

Large language models in psychiatry: Opportunities and challenges.

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.

Generative AI for precision neuroimaging biomarker development in psychiatry.

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.

[Transidentites : the relevance of involving psychiatrists]. / Transidentités : pertinence de l'implication des psychiatres.

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.

Les psychiatres psychothérapeutes jouent un rôle essentiel pour évaluer les demandes et les indications au traitement des personnes souffrant d'incongruence de genre, et les accompagner dans leur parcours de transition. Leur travail permet d'intégrer les enjeux psychologiques et somatiques de ce cheminement et de soutenir les remaniements identitaires profonds qu'il implique.

The clinical utility and relevance in clinical practice of DSM-5 specifiers for major depressive disorder: A Delphi expert consensus study.

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.