Psychedelic Psychiatry's Brave New World.

After a legally mandated, decades-long global arrest of research on psychedelic drugs, investigation of psychedelics in the context of psychiatric disorders is yielding exciting results. Outcomes of neuroscience and clinical research into 5-Hydroxytryptamine 2A (5-HT2A) receptor agonists, such as psilocybin, show promise for addressing a range of serious disorders, including depression and addiction.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

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.

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.

Thinking computationally in translational psychiatry. A commentary on Neville et al. (2024).

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.

Hospital Capabilities Associated With Behavioral Health Integration Within Emergency Departments.

OBJECTIVES: To identify hospital capabilities associated with behavioral health (BH) processes in emergency departments (EDs). RESEARCH DESIGN: Six hundred two hospital responses to the 2017/2018 National Survey of Healthcare Organizations and Systems were linked to 2017 American Hospital Association Annual Survey data. Separate multivariable regressions estimated how hospital capabilities (the use of quality improvement methods, approaches to disseminate best patient-care practices, barriers to using care delivery innovations, and inpatient beds for psychiatric or substance use) were associated with each of 4 ED-based BH processes: mental health and substance use disorder screening, team-based approaches to BH, telepsychiatry, and direct referrals to community-based BH clinicians. Models controlled for hospital structural characteristics and area-level socioeconomic factors. RESULTS: Most hospitals screened for BH conditions and provided direct referrals to community-based BH clinicians. Approximately half of the hospitals used a team approach to BH. A minority had implemented telepsychiatry. Each additional process used to disseminate best patient-care practices was associated with more screening for BH conditions (an increase of 4.07 points on the screening index, P <0.01) and greater likelihood of using a team approach to BH [4.41 percentage point ( P <0.01) increase]. Hospitals reporting more barriers to the use of care delivery innovations reported less screening and use of a team approach [a decrease of 0.15 points on the screening index ( P <0.01) and 0.28 percentage points reduction in likelihood of team approach use ( P <0.001) for 1-point increase in the barrier index]. CONCLUSIONS: Research and interventions focused on removing innovation barriers or adding processes to disseminate best practices offer a path to accelerate BH integration in hospital EDs.

The BJPsych journals: open for business and faster than a speeding bullet in advancing psychiatry internationally.

The Royal College of Psychiatry journals have an outstanding reputation for excellence, integrity and impact in psychiatry. Facilitated by Cambridge University Press, which is equally steeped in tradition, the family of College journals remains committed to enriching our understanding of mental science and exploring the clinical issues that matter.

General psychiatry, still in no-man's land after all these years.

Mental health services have changed beyond recognition in my 38-year career. In this editorial I reflect on those changes and highlight the issues that undermine patient care and damage staff morale. In particular, modern mental health services have undermined the therapeutic relationship, the bedrock underpinning all psychiatric treatment.

Algorithmic fairness in precision psychiatry: analysis of prediction models in individuals at clinical high risk for psychosis.

BACKGROUND: Computational models offer promising potential for personalised treatment of psychiatric diseases. For their clinical deployment, fairness must be evaluated alongside accuracy. Fairness requires predictive models to not unfairly disadvantage specific demographic groups. Failure to assess model fairness prior to use risks perpetuating healthcare inequalities. Despite its importance, empirical investigation of fairness in predictive models for psychiatry remains scarce. AIMS: To evaluate fairness in prediction models for development of psychosis and functional outcome. METHOD: Using data from the PRONIA study, we examined fairness in 13 published models for prediction of transition to psychosis (n = 11) and functional outcome (n = 2) in people at clinical high risk for psychosis or with recent-onset depression. Using accuracy equality, predictive parity, false-positive error rate balance and false-negative error rate balance, we evaluated relevant fairness aspects for the demographic attributes 'gender' and 'educational attainment' and compared them with the fairness of clinicians' judgements. RESULTS: Our findings indicate systematic bias towards assigning less favourable outcomes to individuals with lower educational attainment in both prediction models and clinicians' judgements, resulting in higher false-positive rates in 7 of 11 models for transition to psychosis. Interestingly, the bias patterns observed in algorithmic predictions were not significantly more pronounced than those in clinicians' predictions. CONCLUSIONS: Educational bias was present in algorithmic and clinicians' predictions, assuming more favourable outcomes for individuals with higher educational level (years of education). This bias might lead to increased stigma and psychosocial burden in patients with lower educational attainment and suboptimal psychosis prevention in those with higher educational attainment.

Artificial intelligence and increasing misinformation.

With the recent advances in artificial intelligence (AI), patients are increasingly exposed to misleading medical information. Generative AI models, including large language models such as ChatGPT, create and modify text, images, audio and video information based on training data. Commercial use of generative AI is expanding rapidly and the public will routinely receive messages created by generative AI. However, generative AI models may be unreliable, routinely make errors and widely spread misinformation. Misinformation created by generative AI about mental illness may include factual errors, nonsense, fabricated sources and dangerous advice. Psychiatrists need to recognise that patients may receive misinformation online, including about medicine and psychiatry.

Precision psychiatry: predicting predictability.

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.

A Narrative Review on Pharmacogenomics in Psychiatry: Scientific Definitions, Principles, and Practical Resources.

PURPOSE/BACKGROUND: Pharmacogenetics (PGx) studies the genetic factors underlying interindividual variability in drug response. Only a few countries around the world are already using PGx testing in psychiatric clinical practice, whereas others are still far from adopting it. The main barrier to the clinical adoption of PGx testing seems to be the limited knowledge among psychiatrists regarding the clinical relevance of specific genetic variants to personalize therapies and the accessibility of PGx data. This review aims at further highlighting the importance of PGx-driven clinical decision making for psychotropic medications and raising psychiatrists' awareness of the value of PGx testing in psychiatry. METHODS/PROCEDURES: We summarize the genes for which substantial evidence exists about the clinical utility of integrating their PGx testing in psychiatry. Specifically, we systematically describe the functional role of clinically relevant allelic variants, their frequency across different ethnic groups, and how they contribute to classify patients in relation to their capability in metabolizing psychotropic drugs. FINDINGS/RESULTS: Briefly, clinical guidelines recommend considering PGx testing of the cytochrome class 2 C9 (CYP2C9), C19 (CYP2C19), and D6 (CYP2D6) genes and the human leukocyte antigen (HLA)-A and -B genes for several psychotropic drugs. IMPLICATIONS/CONCLUSIONS: Extensive studies have been carried out to provide a solid rationale for the inclusion of PGx testing in psychiatry. Comprehensive clinical guidelines are readily accessible to support health care providers in tailoring the prescription of psychotropic drugs based on patient's genotype information. This approach presents a tangible opportunity to significantly improve individual responses to psychiatric medications.

Computational psychiatry: from synapses to sentience.

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.

Burning down the house: reinventing drug discovery in psychiatry for the development of targeted therapies.

Despite advances in neuroscience, limited progress has been made in developing new and better medications for psychiatric disorders. Available treatments in psychiatry rely on a few classes of drugs that have a broad spectrum of activity across disorders with limited understanding of mechanism of action. While the added value of more targeted therapies is apparent, a dearth of pathophysiologic mechanisms exists to support targeted treatments, and where mechanisms have been identified and drugs developed, results have been disappointing. Based on serendipity and early successes that led to the current drug armamentarium, a haunting legacy endures that new drugs should align with outdated and overinclusive diagnostic categories, consistent with the idea that "one size fits all". This legacy has fostered clinical trial designs focused on heterogenous populations of patients with a single diagnosis and non-specific outcome variables. Disturbingly, this approach likely contributed to missed opportunities for drugs targeting the hypothalamic-pituitary-adrenal axis and now inflammation. Indeed, cause-and-effect data support the role of inflammatory processes in neurotransmitter alterations that disrupt specific neurocircuits and related behaviors. This pathway to pathology occurs across disorders and warrants clinical trial designs that enrich for patients with increased inflammation and use primary outcome variables associated with specific effects of inflammation on brain and behavior. Nevertheless, such trial designs have not been routinely employed, and results of anti-inflammatory treatments have been underwhelming. Thus, to accelerate development of targeted therapeutics including in the area of inflammation, regulatory agencies and the pharmaceutical industry must embrace treatments and trials focused on pathophysiologic pathways that impact specific symptom domains in subsets of patients, agnostic to diagnosis. Moreover, closer collaboration among basic and clinical investigators is needed to apply neuroscience knowledge to reveal disease mechanisms that drive psychiatric symptoms. Together, these efforts will support targeted treatments, ultimately leading to new and better therapeutics in psychiatry.

Taking subjectivity seriously: towards a unification of phenomenology, psychiatry, and neuroscience.

Nearly all psychiatric diseases involve alterations in subjective, lived experience. The scientific study of the biological basis of mental illness has generally focused on objective measures and observable behaviors, limiting the potential for our understanding of brain mechanisms of disease states and possible treatments. However, applying methods designed principally to interpret objective behavioral measures to the measurement and extrapolation of subjective states presents a number of challenges. In order to help bridge this gap, we draw on the tradition of phenomenology, a philosophical movement concerned with elucidating the structure of lived experience, which emerged in the early 20th century and influenced philosophy of mind, cognitive science, and psychiatry. A number of early phenomenologically-oriented psychiatrists made influential contributions to the field, but this approach retreated to the background as psychiatry moved towards more operationalized disease classifications. Recently, clinical-phenomenological research and viewpoints have re-emerged in the field. We argue that the potential for phenomenological research and methods to generate productive hypotheses about the neurobiological basis of psychiatric diseases has thus far been underappreciated. Using specific examples drawing on the subjective experience of mania and psychosis, we demonstrate that phenomenologically-oriented clinical studies can generate novel and fruitful propositions for neuroscientific investigation. Additionally, we outline a proposal for more rigorously integrating phenomenological investigations of subjective experience with the methods of modern neuroscience research, advocating a cross-species approach with a key role for human subjects research. Collaborative interaction between phenomenology, psychiatry, and neuroscience has the potential to move these fields towards a unified understanding of the biological basis of mental illness.

Overcoming the translational crisis of contemporary psychiatry - converging phenomenological and spatiotemporal psychopathology.

Despite all neurobiological/neurocomputational progress in psychiatric research, recent authors speak about a 'crisis of contemporary psychiatry'. Some argue that we do not yet know the computational mechanisms underlying the psychopathological symptoms ('crisis of mechanism') while others diagnose a neglect of subjectivity, namely first-person experience ('crisis of subjectivity'). In this perspective, we propose that Phenomenological Psychopathology, due to its focus on first-person experience of space and time, is in an ideal position to address the crisis of subjectivity and, if extended to the brain's spatiotemporal topographic-dynamic structure as key focus of Spatiotemporal Psychopathology, the crisis of mechanism. We demonstrate how the first-person experiences of space and time differ between schizophrenia, mood disorders and anxiety disorders allowing for their differential-diagnosis - this addresses the crisis of subjectivity. Presupposing space and time as shared features of brain, experience, and symptoms as their "common currency", the structure of abnormal space and time experience may also serve as template for the structure of the brain's spatiotemporal neuro-computational mechanisms - this may address the crisis of mechanism. Preliminary scientific evidence in our examples of schizophrenia, bipolar disorder, anxiety disorder, and depression support such clinically relevant spatiotemporal determination of both first-person experience (crisis of subjectivity) and the brain's neuro-computational structure (crisis of mechanism). In conclusion, converging Phenomenological Psychopathology with Spatiotemporal Psychopathology might help to overcome the translational crisis in psychiatry by delineating more fine-grained neuro computational and -phenomenal mechanisms; this offers novel candidate biomarkers for diagnosis and therapy including both pharmacological and non-pharmacological treatment.

Barriers and solutions to the adoption of translational tools for computational psychiatry.

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.

Wilhelm Mayer's follow-up study of Kraepelin's cases of paraphrenia: diagnostic validity in 1921.

As part of his lifelong effort to develop optimal nosologic categories for the non-affective delusional syndromes, in the 1913 8th edition of his textbook, Kraepelin proposed a new diagnosis of paraphrenia presenting with extensive bizarre delusions and auditory hallucinations but no prominent negative symptoms or personality deterioration. He tentatively suggested it was distinct from dementia praecox (DP). His proposal was met with controversy. In an attempt to resolve this matter, Wilhelm Mayer, working with Kraepelin in Munich, published in 1921 the result of a follow-up study of the 78 cases of paraphrenia on the basis of which Kraepelin had developed his new diagnosis. In the 74 cases with adequate follow-up, Mayer's final diagnoses were 43% DP, 38% paraphrenia, and 18% other. He also presented limited family data, suggesting co-aggregation of DP and paraphrenia. On the basis of these results, Mayer argued that paraphrenia was likely better considered to represent a form of DP and not an independent disorder. His opinion was accepted by nearly all subsequent authors. Mayer's work appeared nearly a half-century before the proposal of Robin and Guze for the validation of psychiatric disorders by follow-up and family studies. The idea of deciding psychiatric questions on empirical grounds-rather than on the prestige of debating parties-is not a recent discovery but can be traced to the roots of our current diagnostic system in the work of Emil Kraepelin and his associates.

A paradigm shift in translational psychiatry through rodent neuroethology.

Mental disorders are a significant cause of disability worldwide. They profoundly affect individuals' well-being and impose a substantial financial burden on societies and governments. However, despite decades of extensive research, the effectiveness of current therapeutics for mental disorders is often not satisfactory or well tolerated by the patient. Moreover, most novel therapeutic candidates fail in clinical testing during the most expensive phases (II and III), which results in the withdrawal of pharma companies from investing in the field. It also brings into question the effectiveness of using animal models in preclinical studies to discover new therapeutic agents and predict their potential for treating mental illnesses in humans. Here, we focus on rodents as animal models and propose that they are essential for preclinical investigations of candidate therapeutic agents' mechanisms of action and for testing their safety and efficiency. Nevertheless, we argue that there is a need for a paradigm shift in the methodologies used to measure animal behavior in laboratory settings. Specifically, behavioral readouts obtained from short, highly controlled tests in impoverished environments and social contexts as proxies for complex human behavioral disorders might be of limited face validity. Conversely, animal models that are monitored in more naturalistic environments over long periods display complex and ethologically relevant behaviors that reflect evolutionarily conserved endophenotypes of translational value. We present how semi-natural setups in which groups of mice are individually tagged, and video recorded continuously can be attainable and affordable. Moreover, novel open-source machine-learning techniques for pose estimation enable continuous and automatic tracking of individual body parts in groups of rodents over long periods. The trajectories of each individual animal can further be subjected to supervised machine learning algorithms for automatic detection of specific behaviors (e.g., chasing, biting, or fleeing) or unsupervised automatic detection of behavioral motifs (e.g., stereotypical movements that might be harder to name or label manually). Compared to studies of animals in the wild, semi-natural environments are more compatible with neural and genetic manipulation techniques. As such, they can be used to study the neurobiological mechanisms underlying naturalistic behavior. Hence, we suggest that such a paradigm possesses the best out of classical ethology and the reductive behaviorist approach and may provide a breakthrough in discovering new efficient therapies for mental illnesses.

Psychiatrists' perceptions of and reactions to a simulated psychiatric genetic counseling session.

Psychiatric genetic counseling (pGC) has been demonstrated to have meaningful positive outcomes for people with psychiatric conditions and their families. However, it is not widely accessed, and clinical genetics services tend to receive few referrals for these indications. Little research has evaluated psychiatrists' perceptions of and experience with interfacing with pGC. Therefore, we invited Ontario-based psychiatrists to participate in a study in which they first watched a simulated pGC session (representative of typical practice: the patient had depression with no exceptionally dense family history of psychiatric conditions, no genetic testing is provided, and no family-based risk assessment is performed), then completed zoom-based qualitative semi-structured interviews. Interviews were recorded, transcribed verbatim and checked for accuracy. Using interpretive description to analyze interviews with 12 psychiatrists (data collection was stopped at this point, as theoretical sufficiency was achieved), we generated two theoretical models: the first described the decision-making pathway psychiatrists currently follow when determining whether and how to address genetics with a patient; the second described psychiatrists' ideas for integrating pGC into care models for the future. Our data shed light on how to facilitate the delivery of pGC for people with psychiatric conditions and their families.

Editorial: Prevention is the best cure - or is it? A cautionary tale.

In large parts of the Western world prevention is considered a necessary, core component of successful youth care practice. Yet, mental health problems in young people do not appear to have declined over the past decades. How to explain this paradox? In this editorial for the Journal of Child Psychology and Psychiatry, several possible explanations are explored, one of which centers around how prevention is being operationalized-primarily, nowadays, as a screen-and-resolve 'troubleshooting' approach, rather than as an approach that supports the development of good health, competence, and resilience.