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Plain english summary Traditional eating disorder (ED) treatment approaches often use a “one-size-fits-all” method, despite the fact EDs are complex and can vary greatly from person to person. This review discusses how personalised treatment can transform care for people with EDs. Personalised care tailors treatment to each person’s unique biology, mental health, and life circumstances, with the understanding that a more flexible and individualised approach could lead to better outcomes. We explore new discoveries in genetic research, machine learning, and advanced tracking methods to predict how someone might respond to specific treatments and identify what works best for them. We also emphasise the importance of addressing changes in the illness experience over time and including patients’ perspectives in their care. While these approaches show great promise, challenges remain, such as ensuring we have evidence to guide effective personalisation, and that treatments are ethical, widely available and easy for clinicians to use. The paper highlights a future where ED treatments are more precise, effective, and adapted to the individual, offering new hope for recovery. Eating disorders (EDs) are complex and heterogeneous conditions, which are often not resolved with conventional, manualised treatments. Arguments for the development of holistic, person-centred treatments accounting for individual variability have been mounting amongst researchers, clinicians and people with lived experience alike. This review explores the transformative potential of personalised medicine in ED care, emphasising the integration of precision diagnostics and tailored interventions based on individual genetic, biological, psychological and environmental profiles. Building on advancements in genomics, neurobiology, and computational technologies, it advocates for a shift from categorical diagnostic frameworks to symptom-based and dimensional approaches. The paper summarises emerging evidence supporting precision psychiatry, including the development of biomarkers, patient-reported outcomes, predictive modelling, and staging models, and discusses their application in ED research and clinical care. It highlights the utility of machine learning and idiographic statistical methods in optimising therapeutic outcomes and identifies key challenges, such as ethical considerations, scalability and implementation.

For over a decade, the mental health field has been interested in precision treatment using psychopharmacological interventions. More recently, this interest has expanded to include psychotherapy, which is the primary treatment modality for eating disorders. Personalised medicine and precision treatment are also seen as priorities for the eating disorder field by those with lived experience and carers, clinicians and researchers. However, precision treatment necessitates the collection of large amounts of clinical data. Three frameworks exist or have been proposed for the purpose of gathering large-scale routine clinical outcomes in eating disorder services: The International Consortium for Health Outcomes Measurement (ICHOM) eating disorder set, the Australia national minimum dataset, and the Eating Disorders Clinical Research Network. Despite the emergence of these frameworks, challenges exist with implementation. This paper outlines the rationale for the collection of routine outcome data in eating disorder treatment settings, the three existing frameworks proposed, and considerations for implementation and scaling. These include clinical and practice applications, technical aspects, statistics, and contextual factors. We invite attention to our recommendations and collaborative approaches to facilitate progress towards precision treatment in eating disorders. Plain English summary Precision treatment, also known as precision medicine, involves tailoring treatment to the individual characteristics of each patient. Precision treatment for eating disorders is seen as a priority by individuals with lived experience, their carers, clinicians and researchers. However, precision treatment depends on large amounts of clinical data being collected. Currently, eating disorder services do not collect the same information from or about patients. There is no large clinical database to inform precision medicine decisions. Three main frameworks have been proposed to support largescale and consistent data collection in eating disorder services: The International Consortium for Health Outcomes Measurement (ICHOM) eating disorder set, the Australian national minimum dataset, and the UK Eating Disorders Clinical Research Network. These frameworks hold promise but there are challenges with applying them. This paper summarises why collecting routine outcome data is important, the three main frameworks proposed, and the factors which may help to progress data collection and precision treatment for eating disorders. We consider clinical, practical, technical, statistical and contextual factors. It is important that progress in this area is collaborative and involves individuals with lived experience, carers, clinicians and researchers.

Purpose of Review Poor treatment response is a hallmark of major depressive disorder. To tackle this problem, recent neuroimaging studies have sought to characterize antidepressant response in terms of pretreatment differences in intrinsic functional brain networks. Our aim is to review recent studies that predict antidepressant response using intrinsic network connectivity. We discuss current methodological limitations and directions for future antidepressant biomarker studies. Recent Findings Functional connectivity stemming from the subgenual and rostral anterior cingulate has shown particular consistency in predicting antidepressant response. Differences in this connectivity may prove fruitful in differentiating treatment responders to many antidepressant interventions. Future biomarker studies should integrate biological MDD subtypes to address the disorder’s inherent clinical heterogeneity. Summary These clinical and scientific advancements have the potential to address this population marked by limited treatment response. Methodological considerations, including patient selection, response criteria, and model overfitting, will require future investigation to ensure that biomarkers generalize for prospective prediction of treatment response.

Background Precision medicine is a new and important topic in psychiatry. Psychiatry has not yet benefited from the advanced diagnostic and therapeutic technologies that form an integral part of other clinical specialties. Thus, the vision of precision medicine as applied to psychiatry – ‘precision psychiatry’ – promises to be even more transformative than in other fields of medicine, which have already lessened the translational gap. Discussion Herein, we describe ‘precision psychiatry’ and how its several implications promise to transform the psychiatric landscape. We pay particular attention to biomarkers and to how the development of new technologies now makes their discovery possible and timely. The adoption of the term ‘precision psychiatry’ will help propel the field, since the current term ‘precision medicine’, as applied to psychiatry, is impractical and does not appropriately distinguish the field. Naming the field ‘precision psychiatry’ will help establish a stronger, unique identity to what promises to be the most important area in psychiatry in years to come. Conclusion In summary, we provide a wide-angle lens overview of what this new field is, suggest how to propel the field forward, and provide a vision of the near future, with ‘precision psychiatry’ representing a paradigm shift that promises to change the landscape of how psychiatry is currently conceived.

A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.

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.

Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) is an established treatment for refractory depression, however, therapeutic outcomes vary. Mounting evidence suggests that clinical response relates to functional connectivity with the subgenual cingulate cortex (SGC) at the precise DLPFC stimulation site. Critically, SGC‐related network architecture shows considerable interindividual variation across the spatial extent of the DLPFC, indicating that connectivity‐based target personalization could potentially be necessary to improve treatment outcomes. However, to date accurate personalization has not appeared feasible, with recent work indicating that the intraindividual reproducibility of optimal targets is limited to 3.5 cm. Here we developed reliable and accurate methodologies to compute individualized connectivity‐guided stimulation targets. In resting‐state functional MRI scans acquired across 1,000 healthy adults, we demonstrate that, using this approach, personalized targets can be reliably and robustly pinpointed, with a median accuracy of ~2 mm between scans repeated across separate days. These targets remained highly stable, even after 1 year, with a median intraindividual distance between coordinates of only 2.7 mm. Interindividual spatial variation in personalized targets exceeded intraindividual variation by a factor of up to 6.85, suggesting that personalized targets did not trivially converge to a group‐average site. Moreover, personalized targets were heritable, suggesting that connectivity‐guided rTMS personalization is stable over time and under genetic control. This computational framework provides capacity for personalized connectivity‐guided TMS targets to be robustly computed with high precision and has the flexibly to advance research in other basic research and clinical applications. Transcranial magnetic stimulation (TMS) provides an important therapeutic option for treatment resistant depression. Prior research demonstrates that clinical outcomes to TMS could likely be enhanced by personalized treatment that is targeted to specific brain connections. Here we designed innovative methodology which enables these connections to be identified and targeted using TMS at a person‐specific level with unprecedented precision.

BackgroundIrremediability is a key requirement for euthanasia and assisted suicide for psychiatric disorders (psychiatric EAS). Countries like the Netherlands and Belgium ask clinicians to assess irremediability in light of the patient's diagnosis and prognosis and ‘according to current medical understanding’. Clarifying the relevance of a default objective standard for irremediability when applied to psychiatric EAS is crucial for solid policymaking. Yet so far, a thorough examination of this standard is lacking.MethodsUsing treatment-resistant depression (TRD) as a test case, through a scoping review in PubMed, we analyzed the state-of-the-art evidence for whether clinicians can accurately predict individual long-term outcome and single out irremediable cases, by examining the following questions: (1) What is the definition of TRD; (2) What are group-level long-term outcomes of TRD; and (3) Can clinicians make accurate individual outcome predictions in TRD?ResultsA uniform definition of TRD is lacking, with over 150 existing definitions, mostly focused on psychopharmacological research. Available yet limited studies about long-term outcomes indicate that a majority of patients with long-term TRD show significant improvement over time. Finally, evidence about individual predictions in TRD using precision medicine is growing, but methodological shortcomings and varying predictive accuracies pose important challenges for its implementation in clinical practice.ConclusionOur findings support the claim that, as per available evidence, clinicians cannot accurately predict long-term chances of recovery in a particular patient with TRD. This means that the objective standard for irremediability cannot be met, with implications for policy and practice of psychiatric EAS.

Precision psychiatry has emerged as part of the shift to personalized medicine and builds on frameworks such as the U.S. National Institute of Mental Health Research Domain Criteria (RDoC), multilevel biological “omics” data and, most recently, computational psychiatry. The shift is prompted by the realization that a one-size-fits all approach is inadequate to guide clinical care because people differ in ways that are not captured by broad diagnostic categories. One of the first steps in developing this personalized approach to treatment was the use of genetic markers to guide pharmacotherapeutics based on predictions of pharmacological response or non-response, and the potential risk of adverse drug reactions. Advances in technology have made a greater degree of specificity or precision potentially more attainable. To date, however, the search for precision has largely focused on biological parameters. Psychiatric disorders involve multi-level dynamics that require measures of phenomenological, psychological, behavioral, social structural, and cultural dimensions. This points to the need to develop more fine-grained analyses of experience, self-construal, illness narratives, interpersonal interactional dynamics, and social contexts and determinants of health. In this paper, we review the limitations of precision psychiatry arguing that it cannot reach its goal if it does not include core elements of the processes that give rise to psychopathological states, which include the agency and experience of the person. Drawing from contemporary systems biology, social epidemiology, developmental psychology, and cognitive science, we propose a cultural-ecosocial approach to integrating precision psychiatry with person-centered care.

International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics. To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity. Participants with MDD ( = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results. Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%. The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.