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Mezzomo G, Rosa AR. Open science in biomedicine: Overcoming barriers in omics studies. Eur Neuropsychopharmacol 2024; 86:46-48. [PMID: 38936191 DOI: 10.1016/j.euroneuro.2024.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 06/29/2024]
Affiliation(s)
- Giovana Mezzomo
- Laboratory of Molecular Psychiatry, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil; Department of Pharmacology and Graduate Program of Pharmacology and Therapeutics, Institute for Basic Medical Science, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Adriane R Rosa
- Laboratory of Molecular Psychiatry, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil; Department of Pharmacology and Graduate Program of Pharmacology and Therapeutics, Institute for Basic Medical Science, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil; Graduate Program in Biological Sciences: Pharmacology and Therapeutics, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
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van Dellen E. Precision psychiatry: predicting predictability. Psychol Med 2024; 54:1500-1509. [PMID: 38497091 DOI: 10.1017/s0033291724000370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
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.
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Affiliation(s)
- Edwin van Dellen
- Department of Psychiatry and University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
- Department of Neurology, UZ Brussel and Vrije Universiteit Brussel, Brussels, Belgium
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Dhamala E, Yeo BTT, Holmes AJ. One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry. Biol Psychiatry 2023; 93:717-728. [PMID: 36577634 DOI: 10.1016/j.biopsych.2022.09.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 09/07/2022] [Accepted: 09/23/2022] [Indexed: 12/30/2022]
Abstract
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.
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Affiliation(s)
- Elvisha Dhamala
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut.
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut.
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Abstract
Precision psychiatry is currently described as an approach that would bring significant advance to psychiatric clinical practice. Theaim of this article is to investigate Precision Psychiatry's promise for the future; should we substantially invest in this new approach? Thearticle is based on a review of the literature and reports a conceptual analysis. A critical examination of Precision Psychiatry's foundationsshows us that its fundaments are obsolete and flawed: we cannot reduce mental suffering to essences in biology. It is problematic to statethat biological processes hold and capture qualia and meaning, and in themselves and without context would hold and capture somethinglike abnormality. Despite its good efforts, precision psychiatry does not represent a sufficiently promising alternative to the phenotyping thatcomes with the current classification systems.
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Affiliation(s)
- Annemarie C J Köhne
- Department of Psychiatry, Academic Medical Centre in Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry, UMC Utrecht Brain Centre, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jim van Os
- Department of Psychiatry, UMC Utrecht Brain Centre, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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Deif R, Salama M. Depression From a Precision Mental Health Perspective: Utilizing Personalized Conceptualizations to Guide Personalized Treatments. Front Psychiatry 2021; 12:650318. [PMID: 34045980 PMCID: PMC8144285 DOI: 10.3389/fpsyt.2021.650318] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 04/13/2021] [Indexed: 11/13/2022] Open
Abstract
Modern research has proven that the "typical patient" requiring standardized treatments does not exist, reflecting the need for more personalized approaches for managing individual clinical profiles rather than broad diagnoses. In this regard, precision psychiatry has emerged focusing on enhancing prevention, diagnosis, and treatment of psychiatric disorders through identifying clinical subgroups, suggesting personalized evidence-based interventions, assessing the effectiveness of different interventions, and identifying risk and protective factors for remission, relapse, and vulnerability. Literature shows that recent advances in the field of precision psychiatry are rapidly becoming more data-driven reflecting both the significance and the continuous need for translational research in mental health. Different etiologies underlying depression have been theorized and some factors have been identified including neural circuitry, biotypes, biopsychosocial markers, genetics, and metabolomics which have shown to explain individual differences in pathology and response to treatment. Although the precision approach may prove to enhance diagnosis and treatment decisions, major challenges are hindering its clinical translation. These include the clinical diversity of psychiatric disorders, the technical complexity and costs of multiomics data, and the need for specialized training in precision health for healthcare staff, besides ethical concerns such as protecting the privacy and security of patients' data and maintaining health equity. The aim of this review is to provide an overview of recent findings in the conceptualization and treatment of depression from a precision mental health perspective and to discuss potential challenges and future directions in the application of precision psychiatry for the treatment of depression.
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Affiliation(s)
- Reem Deif
- Institute of Global Health and Human Ecology, School of Sciences and Engineering, The American University in Cairo, Cairo, Egypt
| | - Mohamed Salama
- Institute of Global Health and Human Ecology, School of Sciences and Engineering, The American University in Cairo, Cairo, Egypt
- Faculty of Medicine, Mansoura University, Mansoura, Egypt
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
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Kellett S, Easton K, Cooper M, Millings A, Simmonds-Buckley M, Parry G. Evaluation of a Mobile App to Enhance Relational Awareness and Change During Cognitive Analytic Therapy: Mixed Methods Case Series. JMIR Ment Health 2020; 7:e19888. [PMID: 33337342 PMCID: PMC7775821 DOI: 10.2196/19888] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 07/28/2020] [Accepted: 08/16/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND There has been a lack of technological innovation regarding improving the delivery of integrative psychotherapies. This project sought to evaluate an app designed to replace previous paper-based methods supporting relational awareness and change during cognitive analytic therapy (CAT). OBJECTIVE We aimed to assess patients' and therapists' experience of using the technology (ie, the "CAT-App") and to evaluate the relationship between app usage and clinical outcome. METHODS The design was a mixed methods case series. Patients completed the Clinical Outcomes in Routine Evaluation-Outcome Measure pre- and post-CAT. Mood data plus the frequency and effectiveness of relational awareness and change were collected via the app. Therapists and patients were interviewed about their experiences using the app. RESULTS Ten patients (treated by 3 therapists) were enrolled; seven completed treatment and 4 had a reliable improvement in their mental health. App usage and mood change did not differ according to clinical outcome, but there was a statistically significant difference in app usage between completers and dropouts. The qualitative themes described by the therapists were (1) the challenge of incorporating the technology into their clinical practice and (2) the barriers and benefits of the technology. Clients' themes were (1) data protection, (2) motivation and engagement, and (3) restrictions versus flexibility. CONCLUSIONS The CAT-App is capable of supporting relational awareness and change and is an upgrade on older, paper-based formats. Further clinical evaluation is required.
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Affiliation(s)
- Stephen Kellett
- Sheffield Health & Social Care NHS Foundation Trust, University of Sheffield, Sheffield, United Kingdom
| | | | - Martin Cooper
- Sheffield Hallam University, Sheffield, United Kingdom
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