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Østergaard SD, Reinecke-Tellefsen CJ, Brunø AH, Devantier TA, Kølbæk P. Positive predictive value of an ICD-10-based operationalization of bipolar II disorder for register-based research. Nord J Psychiatry 2025; 79:259-263. [PMID: 40178332 DOI: 10.1080/08039488.2025.2483749] [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] [Received: 01/29/2025] [Revised: 03/13/2025] [Accepted: 03/20/2025] [Indexed: 04/05/2025]
Abstract
OBJECTIVE Bipolar II disorder (BD-II) is a subtype of bipolar disorder characterized by recurrent episodes of depression and hypomania, without full manic episodes. Unfortunately, BD-II is not included as a diagnostic code in the ICD-10, which means that the Danish Psychiatric Central Research Register (DPCRR), where diagnoses are recorded according to the ICD-10, cannot be used to study BD-II without further ado. The aim of this study was to investigate whether BD-II can be operationalized retrospectively based on ICD-10 diagnoses with sufficient positive predictive value to allow for studies of BD-II using data from the DPCRR. MATERIALS AND METHODS We operationalized BD-II a priori based on a set of criteria (e.g. a minimum of two mood episodes labelled with ICD-10 diagnostic codes of hypomania or bipolar depression - at least one being bipolar depression - and no manic/mixed episodes). The positive predictive value of this operationalization was then examined by reviewing (two independent reviewers) the electronic health records (EHRs) of 147 patients from the Psychiatric Services of the Central Denmark Region matching the ICD-10-based operationalization of BD-II. RESULTS For 107 of the 147 patients, the EHR review confirmed that BD-II was the most likely diagnosis, resulting in a positive predictive value of 73% for the ICD-10-based operationalization of BD-II. CONCLUSIONS This study suggests that, while not perfect in terms of positive predictive value, the proposed ICD-10-based operationalization will allow for studies of 'predominantly BD-II' using data from the DPCRR with sufficient validity.
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Affiliation(s)
- Søren Dinesen Østergaard
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
| | - Christian Jon Reinecke-Tellefsen
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
| | - Anne Hostrup Brunø
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
| | - Torben Albert Devantier
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
| | - Pernille Kølbæk
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
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2
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Miranda-Mendizabal A, Vetter D, Zambrano J, Zarp J, Chavarría V, Giménez-Palomo A, Gonzalez-Campos M, Valenti M, Walczer Baldinazzo L, Siddi S, Ferrari M, Weissmann D, Henry C, Haro JM, Vedel Kessing L, Vieta E. RNA editing-based biomarker blood test for the diagnosis of bipolar disorder: protocol of the EDIT-B study. Ann Gen Psychiatry 2025; 24:7. [PMID: 39915772 PMCID: PMC11803998 DOI: 10.1186/s12991-024-00544-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2024] [Accepted: 12/31/2024] [Indexed: 02/09/2025] Open
Abstract
INTRODUCTION Misdiagnosis of bipolar disorder (BD) can lead to ineffective treatment, increased risk of manic episodes, and increased severity. Objective diagnostic tests or precise tools to diagnose BD and distinguish it from major depressive disorder (MDD) in depressed patients are lacking. AIM To assess the external diagnostic validity of a blood-based test using an RNA epigenetic signature for the differential diagnosis of BD versus MDD in patients with depression. METHODS AND ANALYSIS Multicentre cross-sectional study including an adult sample of inpatients or outpatients diagnosed with BD or MDD, currently treated for a major depressive episode. A structured diagnostic interview based on validated scales will be conducted. Sociodemographic variables, clinical history, toxic consumption, current treatment and quality of life will be assessed. Blood samples will be obtained and stored at -80 °C until RNA sequencing analysis. The EDIT-B is a blood-based test that combines RNA editing biomarkers and individual data (e.g., age, sex, and tobacco consumption). The clinical validation performance of the EDIT-B will be evaluated using the area under the curve, sensitivity, specificity, positive and negative predictive values, and likelihood ratios. ETHICS AND DISSEMINATION The principles of the Declaration of Helsinki 2013, precision psychiatry research and good clinical practice will be followed. The Research Ethics Committees of the participating centres approved the study. Participants will receive an information sheet and must sign the informed consent before the interview. Participants' data will be pseudonymized at the research sites. Any publication will use fully anonymized data. Publications with the final study results will be disseminated in international peer-reviewed journals and presented at international conferences. STUDY REGISTRATION This study has been registered on clinicaltrials.gov (NCT05603819). Registration date: 28-10-2022.
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Affiliation(s)
- Andrea Miranda-Mendizabal
- Impact and Prevention of Mental Disorders Research Group, Sant Joan de Déu Research Institut, Santa Rosa, 39-57, 08950, Esplugues de Llobregat, Spain.
- Mental Health Networking Biomedical Research Centre (CIBERSAM), Madrid, Spain.
| | - Diana Vetter
- ALCEDIAG/Sys2Diag, CNRS UMR 9005, 1682 rue de la Valsière, Parc Euromédecine, 34188, Montpellier, France.
| | | | - Jeff Zarp
- Copenhagen Affective Disorder Research Centre (CADIC), Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Victor Chavarría
- Impact and Prevention of Mental Disorders Research Group, Sant Joan de Déu Research Institut, Santa Rosa, 39-57, 08950, Esplugues de Llobregat, Spain
- Acute Psychiatric Unit, Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
- Research Network on Chronicity, Primary Care, and Health Promotion and Prevention (RICAPPS), Madrid, Spain
| | - Anna Giménez-Palomo
- Mental Health Networking Biomedical Research Centre (CIBERSAM), Madrid, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Barcelona, Spain
- Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Meritxell Gonzalez-Campos
- Mental Health Networking Biomedical Research Centre (CIBERSAM), Madrid, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Barcelona, Spain
- Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Marc Valenti
- Mental Health Networking Biomedical Research Centre (CIBERSAM), Madrid, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Barcelona, Spain
- Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | | | - Sara Siddi
- Impact and Prevention of Mental Disorders Research Group, Sant Joan de Déu Research Institut, Santa Rosa, 39-57, 08950, Esplugues de Llobregat, Spain
- Mental Health Networking Biomedical Research Centre (CIBERSAM), Madrid, Spain
- Teaching, Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
| | - Maurizio Ferrari
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), SYNLAB SDN, Naples, Italy
| | - Dinah Weissmann
- ALCEDIAG/Sys2Diag, CNRS UMR 9005, 1682 rue de la Valsière, Parc Euromédecine, 34188, Montpellier, France
| | - Chantal Henry
- GHU Psychiatrie & Neurosciences, Paris, France
- Université Paris Cité, Paris, France
| | - Josep Maria Haro
- Impact and Prevention of Mental Disorders Research Group, Sant Joan de Déu Research Institut, Santa Rosa, 39-57, 08950, Esplugues de Llobregat, Spain
- Mental Health Networking Biomedical Research Centre (CIBERSAM), Madrid, Spain
- Teaching, Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
- University of Barcelona, Barcelona, Spain
| | - Lars Vedel Kessing
- Copenhagen Affective Disorder Research Centre (CADIC), Psychiatric Centre Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Eduard Vieta
- Mental Health Networking Biomedical Research Centre (CIBERSAM), Madrid, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Barcelona, Spain
- Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
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Bryant AG, Aquino K, Parkes L, Fornito A, Fulcher BD. Extracting interpretable signatures of whole-brain dynamics through systematic comparison. PLoS Comput Biol 2024; 20:e1012692. [PMID: 39715231 PMCID: PMC11706466 DOI: 10.1371/journal.pcbi.1012692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Revised: 01/07/2025] [Accepted: 12/03/2024] [Indexed: 12/25/2024] Open
Abstract
The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.
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Affiliation(s)
- Annie G. Bryant
- School of Physics, The University of Sydney, Camperdown, New South Wales, Australia
| | - Kevin Aquino
- School of Physics, The University of Sydney, Camperdown, New South Wales, Australia
- Brain Key Incorporated, San Francisco, California, United States of America
| | - Linden Parkes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, New Jersey, United States of America
- School of Psychological Sciences, Turner Institute for Brain and Mental Health & Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Alex Fornito
- School of Psychological Sciences, Turner Institute for Brain and Mental Health & Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, Camperdown, New South Wales, Australia
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Koutsouleris N, Fusar-Poli P. From Heterogeneity to Precision: Redefining Diagnosis, Prognosis, and Treatment of Mental Disorders. Biol Psychiatry 2024; 96:508-510. [PMID: 39232589 DOI: 10.1016/j.biopsych.2024.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Accepted: 07/17/2024] [Indexed: 09/06/2024]
Affiliation(s)
- Nikolaos Koutsouleris
- Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, University Medical Centre, Ludwig-Maximilians-University, Munich, Germany; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom; Max Planck Institute of Psychiatry, Munich, Germany.
| | - Paolo Fusar-Poli
- Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, University Medical Centre, Ludwig-Maximilians-University, Munich, Germany; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom; Department of Brain and Behavioural Sciences, University of Pavia, Italy
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Pavlichenko A, Petrova N, Stolyarov A. The Modern Concept of Schizoaffective Disorder: A Narrative Review. CONSORTIUM PSYCHIATRICUM 2024; 5:42-55. [PMID: 39526012 PMCID: PMC11542913 DOI: 10.17816/cp15513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 09/03/2024] [Indexed: 11/16/2024] Open
Abstract
BACKGROUND Schizoaffective disorder (SAD) is one of the most complex and controversial diagnoses in clinical psychiatry. Despite the significant changes that have occurred in the conceptualization of SAD in modern classifications and the publications of recent years, many unresolved issues remain regarding the disease, from the point of view of clinical psychiatry and basic neuroscience. AIM The purpose of this paper is to summarize published data on the concept of SAD, its clinical characteristics, cognitive profile, potential biomarkers, as well as the place of the disease in the following modern international classifications: the International Classification of Diseases (ICD) 9th, 10th and 11th revisions, and the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5). METHODS We undertook a review of the scientific studies in the relevant bibliographic systems and databases (eLIBRARY, PubMed) of the past 15 years. The descriptive analysis method was used to summarize the collected information. A total of 70 publications were selected for review, including different versions of international classifications of diseases (ICD and DSM-5). RESULTS There has been some improvement in the inter-rater reliability of SAD criteria in modern classifications, but this has not yet led to a clearer understanding among mental health specialists, while the various subtypes of SAD identified so far fail to account for the heterogeneity in the clinical presentation of the disorder. The dimensional approach to diagnosing SAD, according to which the intensity of psychotic and affective symptoms can fluctuate over time and they can influence one another, more accurately reflects the disease's ability to embody different forms. Basic research does not support the identification of a distinct cognitive, neuroimaging, or immunological SAD endophenotype that differs qualitatively from schizophrenia and affective psychoses. CONCLUSION The conceptualization of SAD remains incomplete, and new approaches rooted in neuroscience are needed to better understand the coexistence of affective and psychotic symptoms.
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Anmella G, De Prisco M, Joyce JB, Valenzuela-Pascual C, Mas-Musons A, Oliva V, Fico G, Chatzisofroniou G, Mishra S, Al-Soleiti M, Corponi F, Giménez-Palomo A, Montejo L, González-Campos M, Popovic D, Pacchiarotti I, Valentí M, Cavero M, Colomer L, Grande I, Benabarre A, Llach CD, Raduà J, McInnis M, Hidalgo-Mazzei D, Frye MA, Murru A, Vieta E. Automated Speech Analysis in Bipolar Disorder: The CALIBER Study Protocol and Preliminary Results. J Clin Med 2024; 13:4997. [PMID: 39274208 PMCID: PMC11396536 DOI: 10.3390/jcm13174997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Revised: 08/06/2024] [Accepted: 08/13/2024] [Indexed: 09/16/2024] Open
Abstract
Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently assessed subjectively. Advances in natural language processing offer opportunities to objectively analyze them. Aims: To (i) correlate speech features with manic-depressive symptom severity in BD, (ii) develop predictive models for diagnostic and treatment outcomes, and (iii) determine the most relevant speech features and tasks for these analyses. Methods: This naturalistic, observational study involved longitudinal audio recordings of BD patients at euthymia, during acute manic/depressive phases, and after-response. Patients participated in clinical evaluations, cognitive tasks, standard text readings, and storytelling. After automatic diarization and transcription, speech features, including acoustics, content, formal aspects, and emotionality, will be extracted. Statistical analyses will (i) correlate speech features with clinical scales, (ii) use lasso logistic regression to develop predictive models, and (iii) identify relevant speech features. Results: Audio recordings from 76 patients (24 manic, 21 depressed, 31 euthymic) were collected. The mean age was 46.0 ± 14.4 years, with 63.2% female. The mean YMRS score for manic patients was 22.9 ± 7.1, reducing to 5.3 ± 5.3 post-response. Depressed patients had a mean HDRS-17 score of 17.1 ± 4.4, decreasing to 3.3 ± 2.8 post-response. Euthymic patients had mean YMRS and HDRS-17 scores of 0.97 ± 1.4 and 3.9 ± 2.9, respectively. Following data pre-processing, including noise reduction and feature extraction, comprehensive statistical analyses will be conducted to explore correlations and develop predictive models. Conclusions: Automated speech analysis in BD could provide objective markers for psychopathological alterations, improving diagnosis, monitoring, and response prediction. This technology could identify subtle alterations, signaling early signs of relapse. Establishing standardized protocols is crucial for creating a global speech cohort, fostering collaboration, and advancing BD understanding.
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Affiliation(s)
- Gerard Anmella
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Michele De Prisco
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
| | - Jeremiah B Joyce
- School of Graduate Medical Education, Mayo Clinic, Rochester, MN 55902, USA
| | - Claudia Valenzuela-Pascual
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Ariadna Mas-Musons
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Vincenzo Oliva
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Giovanna Fico
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | | | - Sanjeev Mishra
- Alix School of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Majd Al-Soleiti
- School of Graduate Medical Education, Mayo Clinic, Rochester, MN 55902, USA
| | - Filippo Corponi
- School of Informatics, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Anna Giménez-Palomo
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Laura Montejo
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Meritxell González-Campos
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Dina Popovic
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Isabella Pacchiarotti
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Marc Valentí
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Myriam Cavero
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Lluc Colomer
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Iria Grande
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Antoni Benabarre
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Cristian-Daniel Llach
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON M5G 1M9, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Joaquim Raduà
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
| | - Melvin McInnis
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Diego Hidalgo-Mazzei
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Mark A Frye
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55905, USA
| | - Andrea Murru
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
| | - Eduard Vieta
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic of Barcelona, 08036 Barcelona, Catalonia, Spain
- Bipolar and Depressive Disorders Unit, Digital Innovation Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Catalonia, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), 08007 Barcelona, Catalonia, Spain
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7
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Hubbard NA, Bauer CCC, Siless V, Auerbach RP, Elam JS, Frosch IR, Henin A, Hofmann SG, Hodge MR, Jones R, Lenzini P, Lo N, Park AT, Pizzagalli DA, Vaz-DeSouza F, Gabrieli JDE, Whitfield-Gabrieli S, Yendiki A, Ghosh SS. The Human Connectome Project of adolescent anxiety and depression dataset. Sci Data 2024; 11:837. [PMID: 39095370 PMCID: PMC11297143 DOI: 10.1038/s41597-024-03629-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 07/09/2024] [Indexed: 08/04/2024] Open
Abstract
This article describes primary data and resources available from the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) study, a novel arm of the Human Connectome Project (HCP). Data were collected from 215 adolescents (14-17 years old), 152 of whom had current diagnoses of anxiety and/or depressive disorders at study intake. Data include cross-sectional structural (T1- and T2-weighted), functional (resting state and three tasks), and diffusion-weighted magnetic resonance images. Both unprocessed and HCP minimally-preprocessed imaging data are available within the data release packages. Adolescent and parent clinical interview data, as well as cognitive and neuropsychological data are also included within these packages. Release packages additionally provide data collected from self-report measures assessing key features of adolescent psychopathology, including: anxious and depressive symptom dimensions, behavioral inhibition/activation, exposure to stressful life events, and risk behaviors. Finally, the release packages include 6- and 12-month longitudinal data acquired from clinical measures. Data are publicly accessible through the National Institute of Mental Health Data Archive (ID: #2505).
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Affiliation(s)
- N A Hubbard
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA.
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA.
| | - C C C Bauer
- Department of Psychology, Northeastern University, Boston, MA, USA
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - V Siless
- Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - R P Auerbach
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - J S Elam
- Department of Neuroscience, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - I R Frosch
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - A Henin
- Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - S G Hofmann
- Department of Psychology, Philipps University of Marburg, DEU, Germany
| | - M R Hodge
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - R Jones
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - P Lenzini
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - N Lo
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - A T Park
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - D A Pizzagalli
- Harvard Medical School, Boston, MA, USA
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
| | - F Vaz-DeSouza
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - J D E Gabrieli
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - S Whitfield-Gabrieli
- Department of Psychology, Northeastern University, Boston, MA, USA
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - A Yendiki
- Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - S S Ghosh
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Harvard Medical School, Boston, MA, USA.
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8
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Ohi K, Tanaka Y, Otowa T, Shimada M, Kaiya H, Nishimura F, Sasaki T, Tanii H, Shioiri T, Hara T. Discrimination between healthy participants and people with panic disorder based on polygenic scores for psychiatric disorders and for intermediate phenotypes using machine learning. Aust N Z J Psychiatry 2024; 58:603-614. [PMID: 38581251 DOI: 10.1177/00048674241242936] [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: 04/08/2024]
Abstract
OBJECTIVE Panic disorder is a modestly heritable condition. Currently, diagnosis is based only on clinical symptoms; identifying objective biomarkers and a more reliable diagnostic procedure is desirable. We investigated whether people with panic disorder can be reliably diagnosed utilizing combinations of multiple polygenic scores for psychiatric disorders and their intermediate phenotypes, compared with single polygenic score approaches, by applying specific machine learning techniques. METHODS Polygenic scores for 48 psychiatric disorders and intermediate phenotypes based on large-scale genome-wide association studies (n = 7556-1,131,881) were calculated for people with panic disorder (n = 718) and healthy controls (n = 1717). Discrimination between people with panic disorder and healthy controls was based on the 48 polygenic scores using five methods for classification: logistic regression, neural networks, quadratic discriminant analysis, random forests and a support vector machine. Differences in discrimination accuracy (area under the curve) due to an increased number of polygenic score combinations and differences in the accuracy across five classifiers were investigated. RESULTS All five classifiers performed relatively well for distinguishing people with panic disorder from healthy controls by increasing the number of polygenic scores. Of the 48 polygenic scores, the polygenic score for anxiety UK Biobank was the most useful for discrimination by the classifiers. In combinations of two or three polygenic scores, the polygenic score for anxiety UK Biobank was included as one of polygenic scores in all classifiers. When all 48 polygenic scores were used in combination, the greatest areas under the curve significantly differed among the five classifiers. Support vector machine and logistic regression had higher accuracy than quadratic discriminant analysis and random forests. For each classifier, the greatest area under the curve was 0.600 ± 0.030 for logistic regression (polygenic score combinations N = 14), 0.591 ± 0.039 for neural networks (N = 9), 0.603 ± 0.033 for quadratic discriminant analysis (N = 10), 0.572 ± 0.039 for random forests (N = 25) and 0.617 ± 0.041 for support vector machine (N = 11). The greatest areas under the curve at the best polygenic score combination significantly differed among the five classifiers. Random forests had the lowest accuracy among classifiers. Support vector machine had higher accuracy than neural networks. CONCLUSIONS These findings suggest that increasing the number of polygenic score combinations up to approximately 10 effectively improved the discrimination accuracy and that support vector machine exhibited greater accuracy among classifiers. However, the discrimination accuracy for panic disorder, when based solely on polygenic score combinations, was found to be modest.
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Affiliation(s)
- Kazutaka Ohi
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
- Department of General Internal Medicine, Kanazawa Medical University, Ishikawa, Japan
| | - Yuta Tanaka
- Department of Intelligence Science and Engineering, Gifu University Graduate School of Natural Science and Technology, Gifu, Japan
| | - Takeshi Otowa
- Department of Psychiatry, East Medical Center, Nagoya City University, Nagoya, Japan
| | - Mihoko Shimada
- Genome Medical Science Project (Toyama), National Center for Global Health and Medicine (NCGM), Tokyo, Japan
| | - Hisanobu Kaiya
- Panic Disorder Research Center, Warakukai Medical Corporation, Tokyo, Japan
| | - Fumichika Nishimura
- Center for Research on Counseling and Support Services, The University of Tokyo, Tokyo, Japan
| | - Tsukasa Sasaki
- Department of Physical and Health Education, Graduate School of Education, The University of Tokyo, Tokyo, Japan
| | - Hisashi Tanii
- Center for Physical and Mental Health, Mie University, Mie, Japan
- Graduate School of Medicine, Department of Health Promotion and Disease Prevention, Mie University, Mie, Japan
| | - Toshiki Shioiri
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Takeshi Hara
- Department of Intelligence Science and Engineering, Gifu University Graduate School of Natural Science and Technology, Gifu, Japan
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9
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Takeda T, Fukudome K, Nakano M, Umehara H, Nakamura K. Reliability and validation of the Japanese version of the cognitive distortion scale. Front Psychol 2024; 14:1261166. [PMID: 38933743 PMCID: PMC11204790 DOI: 10.3389/fpsyg.2023.1261166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 09/18/2023] [Indexed: 06/28/2024] Open
Abstract
The cognitive distortion scale (CDS) is a self-rated measure to assess the degree of cognitive distortion which is 10 thinking errors commonly seen in depression. However, there is no scale to measure 10 types cognitive distortions specific to depression in Japan. Therefore, this study translated the CDS into Japanese (CDS-J), and examined its factor structure, validity, and reliability in a Japanese population. A total of 237 healthy individuals and 39 individuals with depression participated in this study. Confirmatory factor analysis indicated the appropriateness of the CDS-J's 10-factor structure. Regarding convergent validity, CDS-J was significantly correlated with dysfunctional attitudes, negative automatic thoughts, and depression. Regarding discriminant validity, the CDS-J showed no significant correlation with positive automatic thoughts. The total CDS-J scores of the healthy participants and of those with major depression were compared. The results showed significant differences between groups. Finally, the CDS-J was found to have a high test-retest reliability. Therefore, the CDS-J is a valid and reliable tool for assessing cognitive distortions in Japan.
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Affiliation(s)
- Tomoya Takeda
- Department of Psychological Sciences, University of Human Environments, Matsuyama, Japan
| | - Koudai Fukudome
- Department of Human and Social Studies, St. Catherine University, Matsuyama, Japan
| | - Mina Nakano
- Department of Psychology, Fukuyama University, Fukuyama, Japan
| | - Hidehiro Umehara
- Department of Psychiatry, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
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10
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Bryant AG, Aquino K, Parkes L, Fornito A, Fulcher BD. Extracting interpretable signatures of whole-brain dynamics through systematic comparison. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.573372. [PMID: 38915560 PMCID: PMC11195072 DOI: 10.1101/2024.01.10.573372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.
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Affiliation(s)
- Annie G. Bryant
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
| | - Kevin Aquino
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
- Brain Key Incorporated, San Francisco, CA, USA
| | - Linden Parkes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
- Turner Institute for Brain & Mental Health, Monash University, VIC, Australia
| | - Alex Fornito
- Turner Institute for Brain & Mental Health, Monash University, VIC, Australia
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
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11
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Liao Y, Han X, Guo L, Wang W, Wang H, Li L, Shen M, Song W, Zhu D, Jiang Y, Teopiz KM, Lu C, McIntyre RS. Evaluation of a novel instrument for detecting bipolar disorders in China: The Rapid Mood Screener (RMS). J Affect Disord 2024; 348:54-61. [PMID: 38110155 DOI: 10.1016/j.jad.2023.12.034] [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] [Received: 08/11/2023] [Revised: 12/01/2023] [Accepted: 12/13/2023] [Indexed: 12/20/2023]
Abstract
OBJECTIVE Bipolar disorder is easily misdiagnosed with major depressive disorder (MDD). The Rapid Mood Screener (RMS) was developed to address this unmet clinical need. This study aims to translate and evaluated the reliability and validity of the RMS in Chinese adults with bipolar I/II disorder (BD-I/II). METHODS Brislin's translation and Delphi method were conducted to formulate the RMS-Chinses version (RMS-C). Patients with MDD (N = 99), BD-I (N = 77) and BD-II (N = 78) were included to assess the validity and reliability of RMS-C. The area under the curve (AUC) was computed to ascertain the ability of the Mood Disorder Questionnaire (MDQ) and RMS-C to distinguish BD-I and BD-II from MDD. The optimal cut-off scores for classification were also calculated by the maximum sensitivity and specificity. RESULTS The intraclass correlation coefficient of the RMS-C was 0.82 (95%CI, 0.71-0.89). The content validity index by six items were 0.71, 0.86, 1.00, 0.86, 1.00, and 1.00 in turn, and by scales was 0.90. The AUCs of the RMS-C in both BD-I/II, BD-I alone and BD-II alone were 0.83 (95 % CI, 0.78-0.89), 0.82 (95 % CI, 0.75-0.89) and 0.85 (95 % CI, 0.79-0.91), respectively, and were comparably to the MDQ. The optimal RMS-C values of the presence of BD-I and BD-II were >4 and 3, respectively. CONCLUSION The RMS-C is a valid, simple self-administer screening tool to help identify BD-I or BD-II in persons experiencing a depressive episode. Validating the impact of screening with the RMS-C on health outcomes and health economics is warranted.
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Affiliation(s)
- Yuhua Liao
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, People's Republic of China; Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Xue Han
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, People's Republic of China
| | - Lan Guo
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wanxin Wang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Hongqiong Wang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Lingjiang Li
- Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Manjun Shen
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, People's Republic of China
| | - Weidong Song
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, People's Republic of China
| | - Dongjian Zhu
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, People's Republic of China
| | - Yunbin Jiang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Kayla M Teopiz
- Brain and Cognition Discovery Foundation, Toronto, ON, Canada
| | - Ciyong Lu
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China.
| | - Roger S McIntyre
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Department of Pharmacology, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Canadian Rapid Treatment Center of Excellence, Mississauga, ON, Canada; Brain and Cognition Discovery Foundation, Toronto, ON, Canada
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12
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Zhu D, Wang Y, Wang H, Li Z, Li X, Chen X, Su B, Wang Z. Assessment of eye tracking and facial expression as a model for depression identification: A preliminary study. Gen Hosp Psychiatry 2024; 87:144-145. [PMID: 37770292 DOI: 10.1016/j.genhosppsych.2023.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/06/2023] [Accepted: 09/08/2023] [Indexed: 09/30/2023]
Affiliation(s)
- Daren Zhu
- Research Department, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin 300162, PR China; Chongzuo Detachment, Guangxi Corps of Chinese Armed Police Force, Chongzuo, Guangxi 532100, PR China
| | - Yipeng Wang
- Research Department, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin 300162, PR China; Logistics University of Chinese People's Armed Police Force, Tianjin 300300, PR China
| | - Haiyan Wang
- Military Medical Examination and Certification Section, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin 300300, PR China
| | - Zhengchao Li
- Research Department, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin 300162, PR China
| | - XiaoYin Li
- Research Department, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin 300162, PR China
| | - Xuyi Chen
- Research Department, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin 300162, PR China
| | - Bin Su
- Research Department, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin 300162, PR China.
| | - Zhenguo Wang
- Research Department, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin 300162, PR China.
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13
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Mensi MM, Guerini FR, Marchesi M, Chiappedi M, Bolognesi E, Borgatti R. SNAP-25 Polymorphisms in Autism Spectrum Disorder: A Pilot Study towards a Possible Endophenotype. Pediatr Rep 2023; 15:766-773. [PMID: 38133436 PMCID: PMC10747488 DOI: 10.3390/pediatric15040068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/23/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
While there is substantial agreement on the diagnostic criteria for autism spectrum disorder, it is also acknowledged that it has a broad range of clinical presentations. This can complicate the diagnostic process and aggravate the choice of the most suitable rehabilitative strategy for each child. Attentional difficulties are among the most frequently reported comorbidities in autism spectrum disorder. We investigated the role of SNAP-25 polymorphisms. Synaptosome-associated protein 25 (SNAP25) is a presynaptic membrane-binding protein; it plays a crucial role in neurotransmission and has already been studied in numerous psychiatric disorders. It was also seen to be associated with hyperactivity in children with autism spectrum disorder. We collected clinical, behavioral and neuropsychological data on 41 children with a diagnosis of autism spectrum disorder, and then genotyped them for five single-nucleotide polymorphisms of SNAP-25. Participants were divided into two groups according to the Autism Diagnostic Observation Schedule (ADOS-2) Severity Score. In the group with the highest severity score, we found significant associations of clinical data with polymorphism rs363050 (A/G): children with the GG genotype had lower total IQ, more severe autistic functioning and more attentional difficulties. Our research could be the starting point for outlining a possible endophenotype among patients with autism spectrum disorder who are clinically characterized by severe autistic functioning and significant attentional difficulties.
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Affiliation(s)
- Martina Maria Mensi
- Child Neuropsychiatry Unit, IRCCS Mondino Foundation, 27100 Pavia, Italy; (M.M.M.); (R.B.)
| | - Franca Rosa Guerini
- IRCCS Don Carlo Gnocchi Foundation—ONLUS, 20148 Milan, Italy; (F.R.G.); (E.B.)
| | - Michele Marchesi
- Child Neurology and Psychiatry Unit, ASST Pavia, 27029 Vigevano, Italy;
| | - Matteo Chiappedi
- Department of Brain and Behavioural Sciences, University of Pavia, 27100 Pavia, Italy
| | | | - Renato Borgatti
- Child Neuropsychiatry Unit, IRCCS Mondino Foundation, 27100 Pavia, Italy; (M.M.M.); (R.B.)
- Child Neurology and Psychiatry Unit, ASST Pavia, 27029 Vigevano, Italy;
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14
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Dahl A, Thompson M, An U, Krebs M, Appadurai V, Border R, Bacanu SA, Werge T, Flint J, Schork AJ, Sankararaman S, Kendler KS, Cai N. Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder. Nat Genet 2023; 55:2082-2093. [PMID: 37985818 PMCID: PMC10703686 DOI: 10.1038/s41588-023-01559-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 09/18/2023] [Indexed: 11/22/2023]
Abstract
Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.
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Affiliation(s)
- Andrew Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
| | - Michael Thompson
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ulzee An
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Morten Krebs
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
| | - Vivek Appadurai
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
| | - Richard Border
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Silviu-Alin Bacanu
- Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
- Lundbeck Foundation GeoGenetics Centre, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jonathan Flint
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Andrew J Schork
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
- Neurogenomics Division, The Translational Genomics Research Institute (TGEN), Phoenix, AZ, USA
- Section for Geogenetics, GLOBE Institute, Faculty of Health and Medical Sciences, Copenhagen University, Copenhagen, Denmark
| | - Sriram Sankararaman
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Na Cai
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany.
- Computational Health Centre, Helmholtz Zentrum München, Neuherberg, Germany.
- School of Medicine, Technical University of Munich, Munich, Germany.
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15
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Mao K, Wu Y, Chen J. A systematic review on automated clinical depression diagnosis. NPJ MENTAL HEALTH RESEARCH 2023; 2:20. [PMID: 38609509 PMCID: PMC10955993 DOI: 10.1038/s44184-023-00040-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 09/27/2023] [Indexed: 04/14/2024]
Abstract
Assessing mental health disorders and determining treatment can be difficult for a number of reasons, including access to healthcare providers. Assessments and treatments may not be continuous and can be limited by the unpredictable nature of psychiatric symptoms. Machine-learning models using data collected in a clinical setting can improve diagnosis and treatment. Studies have used speech, text, and facial expression analysis to identify depression. Still, more research is needed to address challenges such as the need for multimodality machine-learning models for clinical use. We conducted a review of studies from the past decade that utilized speech, text, and facial expression analysis to detect depression, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline. We provide information on the number of participants, techniques used to assess clinical outcomes, speech-eliciting tasks, machine-learning algorithms, metrics, and other important discoveries for each study. A total of 544 studies were examined, 264 of which satisfied the inclusion criteria. A database has been created containing the query results and a summary of how different features are used to detect depression. While machine learning shows its potential to enhance mental health disorder evaluations, some obstacles must be overcome, especially the requirement for more transparent machine-learning models for clinical purposes. Considering the variety of datasets, feature extraction techniques, and metrics used in this field, guidelines have been provided to collect data and train machine-learning models to guarantee reproducibility and generalizability across different contexts.
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Affiliation(s)
- Kaining Mao
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada
| | - Yuqi Wu
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada
| | - Jie Chen
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada.
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Yamamori Y, Robinson OJ, Roiser JP. Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance. eLife 2023; 12:RP87720. [PMID: 37963085 PMCID: PMC10645421 DOI: 10.7554/elife.87720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023] Open
Abstract
Although avoidance is a prevalent feature of anxiety-related psychopathology, differences in the measurement of avoidance between humans and non-human animals hinder our progress in its theoretical understanding and treatment. To address this, we developed a novel translational measure of anxiety-related avoidance in the form of an approach-avoidance reinforcement learning task, by adapting a paradigm from the non-human animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-avoidance behaviour in this task and investigated how they relate to subjective task-induced anxiety. In a large online study (n = 372), participants who experienced greater task-induced anxiety avoided choices associated with punishment, even when this resulted in lower overall reward. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards. We replicated these findings in an independent sample (n = 627) and we also found fair-to-excellent reliability of measures of task performance in a sub-sample retested 1 week later (n = 57). Our findings demonstrate the potential of approach-avoidance reinforcement learning tasks as translational and computational models of anxiety-related avoidance. Future studies should assess the predictive validity of this approach in clinical samples and experimental manipulations of anxiety.
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Affiliation(s)
- Yumeya Yamamori
- Institute of Cognitive Neuroscience, University College LondonLondonUnited Kingdom
| | - Oliver J Robinson
- Institute of Cognitive Neuroscience, University College LondonLondonUnited Kingdom
- Research Department of Clinical, Educational and Health Psychology, University College LondonLondonUnited Kingdom
| | - Jonathan P Roiser
- Institute of Cognitive Neuroscience, University College LondonLondonUnited Kingdom
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17
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Savage K, Sarris J, Hughes M, Bousman CA, Rossell S, Scholey A, Stough C, Suo C. Neuroimaging Insights: Kava's ( Piper methysticum) Effect on Dorsal Anterior Cingulate Cortex GABA in Generalized Anxiety Disorder. Nutrients 2023; 15:4586. [PMID: 37960239 PMCID: PMC10649338 DOI: 10.3390/nu15214586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/07/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Generalised Anxiety Disorder (GAD) is a prevalent, chronic mental health disorder. The measurement of regional brain gamma-aminobutyric acid (GABA) offers insight into its role in anxiety and is a potential biomarker for treatment response. Research literature suggests Piper methysticum (Kava) is efficacious as an anxiety treatment, but no study has assessed its effects on central GABA levels. This study investigated dorsal anterior cingulate (dACC) GABA levels in 37 adult participants with GAD. GABA was measured using proton magnetic resonance spectroscopy (1H-MRS) at baseline and following an eight-week administration of Kava (standardised to 120 mg kavalactones twice daily) (n = 20) or placebo (n = 17). This study was part of the Kava for the Treatment of GAD (KGAD; ClinicalTrials.gov: NCT02219880), a 16-week intervention study. Compared with the placebo group, the Kava group had a significant reduction in dACC GABA (p = 0.049) at eight weeks. Baseline anxiety scores on the HAM-A were positively correlated with GABA levels but were not significantly related to treatment. Central GABA reductions following Kava treatment may signal an inhibitory effect, which, if considered efficacious, suggests that GABA levels are modulated by Kava, independent of reported anxiety symptoms. dACC GABA patterns suggest a functional role of higher levels in clinical anxiety but warrants further research for symptom benefit. Findings suggest that dACC GABA levels previously un-examined in GAD could serve as a biomarker for diagnosis and treatment response.
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Affiliation(s)
- Karen Savage
- Centre for Human Psychopharmacology, Swinburne University of Technology, 427-451 Burwood Road, Melbourne 3122, Australia
- Florey Institute of Neuroscience and Mental Health, Melbourne University, Melbourne 3121, Australia
| | - Jerome Sarris
- Florey Institute of Neuroscience and Mental Health, Melbourne University, Melbourne 3121, Australia
- NICM Health Research Institute, Western Sydney University, Sydney 2751, Australia
| | - Matthew Hughes
- Centre for Mental Health, Swinburne University of Technology, Melbourne 3122, Australia
| | - Chad A. Bousman
- Departments of Medical Genetics, Psychiatry, Physiology & Pharmacology, and Community Health Sciences, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Susan Rossell
- Centre for Mental Health, Swinburne University of Technology, Melbourne 3122, Australia
- Mental Health, St Vincent’s Hospital Melbourne, Melbourne 3065, Australia
| | - Andrew Scholey
- Centre for Human Psychopharmacology, Swinburne University of Technology, 427-451 Burwood Road, Melbourne 3122, Australia
- Department of Nutrition, Dietetics and Food, Monash University, Melbourne 3168, Australia
| | - Con Stough
- Centre for Human Psychopharmacology, Swinburne University of Technology, 427-451 Burwood Road, Melbourne 3122, Australia
| | - Chao Suo
- Brain Park, Turner Institute of Brain and Mind, Monash University, Melbourne 3800, Australia
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18
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Nöhles VB, Bermpohl F, Falkai P, Reif-Leonhard C, Jessen F, Adli M, Otte C, Meyer-Lindenberg A, Bauer M, Rubarth K, Anghelescu IG, Rujescu D, Correll CU. Patient characteristics, validity of clinical diagnoses and Outcomes Associated with Suicidality in Inpatients with Symptoms of Depression (OASIS-D): design, procedures and outcomes. BMC Psychiatry 2023; 23:744. [PMID: 37828493 PMCID: PMC10571442 DOI: 10.1186/s12888-023-05230-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Suicidality, ranging from passive suicidal thoughts to suicide attempt, is common in major depressive disorder (MDD). However, relatively little is known about patient, illness and treatment characteristics in those with co-occurring MDD and suicidality, including the timing of and factors associated with the offset, continuation or reemergence of suicidality. Here, we present the background, rationale, design and hypotheses of the Patient Characteristics, Validity of Clinical Diagnoses and Outcomes Associated with Suicidality in Inpatients with Symptoms of Depression (OASIS-D) study, an investigator-initiated, observational study, funded by Janssen-Cilag GmbH. METHODS/RESULTS OASIS-D is an eight-site, six-month, cohort study of patients aged 18-75 hospitalized with MDD. Divided into three sub-studies and patient populations (PPs), OASIS-D will (i) systematically characterize approximately 4500 consecutively hospitalized patients with any form of unipolar depressive episode (PP1), (ii) evaluate the validity of the clinical diagnosis of moderate or severe unipolar depressive episode with the Mini-International Neuropsychiatric Interview (M.I.N.I.) and present suicidality (at least passive suicidal thoughts) present ≥ 48 h after admission with the Sheehan-Suicide Tracking Scale (S-STS), assessing also predictors of the diagnostic concordance/discordance of MDD in around 500 inpatients (PP2), and (iii) characterize and prospectively follow for 6 months 315 inpatients with a research-verified moderate or severe unipolar depressive episode and at least passive suicidal thoughts ≥ 48 h after admission, evaluating treatment and illness/response patterns at baseline, hospital discharge, 3 and 6 months. Exploratory objectives will describe the association between the number of days with suicidality and utilization of outpatient and inpatient care services, and structured assessments of factors influencing the risk of self-injurious behavior without suicidal intent, and of continuous, intermittent or remitted suicidality during the 6-month observation period. CONCLUSION Despite their frequency and clinical relevance, relatively little is known about patient and treatment characteristics of individuals with MDD and suicidality, including factors moderating and mediating the outcome of both MDD and suicidality. Results of the OASIS-D study are hoped to improve the understanding of the frequency, correlates and 6-month naturalistic treatment and outcome trajectories of different levels of suicidality in hospitalized adults with MDD and suicidality. TRIAL REGISTRATION NCT04404309 [ClinicalTrials.gov].
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Affiliation(s)
- Viktor B Nöhles
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin - Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Felix Bermpohl
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin - Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Campus St. Hedwig Hospital, Berlin, Germany
| | - Peter Falkai
- Clinic for Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Christine Reif-Leonhard
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Frank Jessen
- Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany
| | - Mazda Adli
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin - Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Campus Mitte, Berlin, Germany
- Center for Psychiatry, Psychotherapy and Psychosomatic Medicine, Fliedner Klinik Berlin, Berlin, Germany
| | - Christian Otte
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin - Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Germany
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Kerstin Rubarth
- Institute of Medical Informatics, Charité Universitätsmedizin - Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Institute of Biometry and Clinical Epidemiology, Charité Universitätsmedizin - Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ion-George Anghelescu
- Clinic for Psychiatry, Psychosomatics and Psychotherapy, Mental Health Institute Berlin, Berlin, Germany
| | - Dan Rujescu
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Christoph U Correll
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin - Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany.
- Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA.
- Department of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
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19
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Restifo S. Existential depression: A meaningful diagnostic entity? Australas Psychiatry 2023; 31:502-504. [PMID: 37288818 DOI: 10.1177/10398562231180492] [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: 06/09/2023]
Abstract
OBJECTIVE To examine the construct of existential depression and whether it represents a distinct diagnostic entity. METHOD Descriptive psychopathology and phenomenology are used to define the characteristics of existential depression and for comparison with other presentations of low mood. RESULTS Existential depression can be differentiated from other forms of depression by careful appraisal of symptomatology. Drawing attention to this, and likewise other distinguishable yet under-recognised forms of depression, may help stimulate interest in further research on the classification of mood disorders with the prospect of greater diagnostic specificity and more precise treatment matching. CONCLUSION Existential depression is a clinically discernible diagnostic entity.
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Affiliation(s)
- Sam Restifo
- Midland Community Mental Health Service, Midland, WA, Australia
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20
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Martino DJ, Valerio MP. A compelling need to empirically validate bipolar depression. Int J Bipolar Disord 2023; 11:15. [PMID: 37115339 PMCID: PMC10147869 DOI: 10.1186/s40345-023-00295-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Affiliation(s)
- Diego J Martino
- National Council of Scientific and Technical Research (CONICET), Charcas 4189, 1º "C" (1425), Buenos Aires, Argentina.
- Institute of Cognitive and Translational Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina.
- Psychiatric Emergencies Hospital Torcuato de Alvear, Av. Warnes 2630, Buenos Aires, Argentina.
| | - Marina P Valerio
- National Council of Scientific and Technical Research (CONICET), Charcas 4189, 1º "C" (1425), Buenos Aires, Argentina
- Psychiatric Emergencies Hospital Torcuato de Alvear, Av. Warnes 2630, Buenos Aires, Argentina
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21
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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: 40] [Impact Index Per Article: 20.0] [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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22
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Wise T, Robinson OJ, Gillan CM. Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling. Biol Psychiatry 2023; 93:690-703. [PMID: 36725393 PMCID: PMC10017264 DOI: 10.1016/j.biopsych.2022.09.034] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 09/09/2022] [Accepted: 09/27/2022] [Indexed: 02/03/2023]
Abstract
Most psychiatric disorders do not occur in isolation, and most psychiatric symptom dimensions are not uniquely expressed within a single diagnostic category. Current treatments fail to work for around 25% to 40% of individuals, perhaps due at least in part to an overreliance on diagnostic categories in treatment development and allocation. In this review, we describe ongoing efforts in the field to surmount these challenges and precisely characterize psychiatric symptom dimensions using large-scale studies of unselected samples via remote, online, and "citizen science" efforts that take a dimensional, mechanistic approach. We discuss the importance that efforts to identify meaningful psychiatric dimensions be coupled with careful computational modeling to formally specify, test, and potentially falsify candidate mechanisms that underlie transdiagnostic symptom dimensions. We refer to this approach, i.e., where symptom dimensions are identified and validated against computationally well-defined neurocognitive processes, as computational factor modeling. We describe in detail some recent applications of this method to understand transdiagnostic cognitive processes that include model-based planning, metacognition, appetitive processing, and uncertainty estimation. In this context, we highlight how computational factor modeling has been used to identify specific associations between cognition and symptom dimensions and reveal previously obscured relationships, how findings generalize to smaller in-person clinical and nonclinical samples, and how the method is being adapted and optimized beyond its original instantiation. Crucially, we discuss next steps for this area of research, highlighting the value of more direct investigations of treatment response that bridge the gap between basic research and the clinic.
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Affiliation(s)
- Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Oliver J Robinson
- Neuroscience and Mental Health Group, Institute of Cognitive Neuroscience, University College London, London, United Kingdom; Research Department of Clinical Education and Health Psychology, University College London, London, United Kingdom
| | - Claire M Gillan
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.
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23
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Okada G, Sakai Y, Shibakawa M, Yoshioka T, Itai E, Shinzato H, Yamamoto O, Kurata K, Tamura T, Jitsuiki H, Yamashita H, Mantani A, Yokota N, Kawato M, Okamoto Y. Examining the usefulness of the brain network marker program using fMRI for the diagnosis and stratification of major depressive disorder: a non-randomized study protocol. BMC Psychiatry 2023; 23:63. [PMID: 36694153 PMCID: PMC9875439 DOI: 10.1186/s12888-023-04560-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Although many studies have reported the biological basis of major depressive disorder (MDD), none have been put into practical use. Recently, we developed a generalizable brain network marker for MDD diagnoses (diagnostic marker) across multiple imaging sites using resting-state functional magnetic resonance imaging (rs-fMRI). We have planned this clinical trial to establish evidence for the practical applicability of this diagnostic marker as a medical device. In addition, we have developed generalizable brain network markers for MDD stratification (stratification markers), and the verification of these brain network markers is a secondary endpoint of this study. METHODS This is a non-randomized, open-label study involving patients with MDD and healthy controls (HCs). We will prospectively acquire rs-fMRI data from 50 patients with MDD and 50 HCs and anterogradely verify whether our diagnostic marker can distinguish between patients with MDD and HCs. Furthermore, we will longitudinally obtain rs-fMRI and clinical data at baseline and 6 weeks later in 80 patients with MDD treated with escitalopram and verify whether it is possible to prospectively distinguish MDD subtypes that are expected to be effectively responsive to escitalopram using our stratification markers. DISCUSSION In this study, we will confirm that sufficient accuracy of the diagnostic marker could be reproduced for data from a prospective clinical study. Using longitudinally obtained data, we will also examine whether the "brain network marker for MDD diagnosis" reflects treatment effects in patients with MDD and whether treatment effects can be predicted by "brain network markers for MDD stratification". Data collected in this study will be extremely important for the clinical application of the brain network markers for MDD diagnosis and stratification. TRIAL REGISTRATION Japan Registry of Clinical Trials ( jRCTs062220063 ). Registered 12/10/2022.
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Affiliation(s)
- Go Okada
- grid.257022.00000 0000 8711 3200Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yuki Sakai
- grid.418163.90000 0001 2291 1583Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan ,XNef, Inc., Kyoto, Japan
| | | | - Toshinori Yoshioka
- grid.418163.90000 0001 2291 1583Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan ,XNef, Inc., Kyoto, Japan
| | - Eri Itai
- grid.257022.00000 0000 8711 3200Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Hotaka Shinzato
- grid.257022.00000 0000 8711 3200Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | | | | | | | | | | | | | | | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan. .,XNef, Inc., Kyoto, Japan.
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
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24
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Carmassi C, Pedrinelli V, Antonini C, Dell'Oste V, Gravina D, Nardi B, Bertelloni CA, Massimetti G, Nieto-Munuera J, Dell'Osso L. Validation of the Spanish Version of the Trauma and Loss Spectrum Self-Report (TALS-SR): A Study on Healthcare Workers Facing the COVID-19 Pandemic. Neuropsychiatr Dis Treat 2023; 19:495-506. [PMID: 36896340 PMCID: PMC9990502 DOI: 10.2147/ndt.s396540] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/17/2022] [Indexed: 03/06/2023] Open
Abstract
Background The present study aimed at reporting about the validity and reliability of the Spanish version of the Trauma and Loss Spectrum-Self Report (TALS-SR), an instrument based on a multidimensional approach to Post-Traumatic Stress Disorder (PTSD) and Prolonged Grief Disorder (PGD), including a range of threatening or traumatic experiences and significant losses, besides the spectrum of peri-traumatic stress reactions and post-traumatic stress symptoms that may occur. Methods A sample of 87 Health Care Workers (HCWs) employed in the COVID-19 Emergency Department at the Virgen de la Arrixaca and Reina Sofia Hospitals (Murcia, Spain) during the pandemic, was consecutively recruited and fulfilled the TALS-SR. Assessments also included the Impact of Event Scale-Revised (IES-R), to examine post-traumatic stress symptoms and probable PTSD. Nineteen HCWs fulfilled the TALS-SR again after three weeks from baseline for test-retest reliability. Results This study provides evidence of good internal consistency and test-retest reliability of the Spanish version of the TALS-SR. Strong support for the internal validity structure was obtained, with positive and significant correlations between the five symptomatologic domains and the symptomatologic total score. Significant and good correlations between the TALS-SR symptomatologic domains and the IES-R total and single domains' scores were found. The Questionnaire also demonstrated to discriminate between subjects with and without PTSD, with subjects with PTSD showing significantly higher mean scores in each domain of the TALS-SR. Conclusion This study validates the Spanish version of TALS-SR, providing a useful instrument for a spectrum approach to PTSD and confirms the potential utility of this psychometric tool in both clinical practice and research settings.
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Affiliation(s)
- Claudia Carmassi
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Virginia Pedrinelli
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.,Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, Italy
| | - Corinna Antonini
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Valerio Dell'Oste
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.,Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, Italy
| | - Davide Gravina
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Benedetta Nardi
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | | | - Gabriele Massimetti
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | | | - Liliana Dell'Osso
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
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25
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Chakrabarti S. Bipolar disorder in the International Classification of Diseases-Eleventh version: A review of the changes, their basis, and usefulness. World J Psychiatry 2022; 12:1335-1355. [PMID: 36579354 PMCID: PMC9791613 DOI: 10.5498/wjp.v12.i12.1335] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/07/2022] [Accepted: 11/22/2022] [Indexed: 12/16/2022] Open
Abstract
The World Health Organization’s 11th revision of the International Classification of Diseases (ICD-11) including the chapter on mental disorders has come into effect this year. This review focuses on the “Bipolar or Related Disorders” section of the ICD-11 draft. It describes the benchmarks for the new version, particularly the foremost principle of clinical utility. The alterations made to the diagnosis of bipolar disorder (BD) are evaluated on their scientific basis and clinical utility. The change in the diagnostic requirements for manic and hypomanic episodes has been much debated. Whether the current criteria have achieved an optimum balance between sensitivity and specificity is still not clear. The ICD-11 definition of depressive episodes is substantially different, but the lack of empirical support for the changes has meant that the reliability and utility of bipolar depression are relatively low. Unlike the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5), the ICD-11 has retained the category of mixed episodes. Although the concept of mixed episodes in the ICD-11 is not perfect, it appears to be more inclusive than the DSM-5 approach. Additionally, there are some uncertainties about the guidelines for the subtypes of BD and cyclothymic disorder. The initial results on the reliability and clinical utility of BD are promising, but the newly created diagnostic categories also appear to have some limitations. Although further improvement and research are needed, the focus should now be on facing the challenges of implementation, dissemination, and education and training in the use of these guidelines.
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Affiliation(s)
- Subho Chakrabarti
- Department of Psychiatry, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, UT, India
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26
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Chen VCH, Wu SI, Lin CF, Lu ML, Chen YL, Stewart R. Association of Prenatal Exposure to Benzodiazepines With Development of Autism Spectrum and Attention-Deficit/Hyperactivity Disorders. JAMA Netw Open 2022; 5:e2243282. [PMID: 36413366 PMCID: PMC9682429 DOI: 10.1001/jamanetworkopen.2022.43282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
IMPORTANCE Prenatal exposure to benzodiazepines is reported to be associated with neurodevelopmental disorders among children, but associations of maternal genetic confounding with neurodevelopmental disorders among children have not been taken into consideration. OBJECTIVE To ascertain whether prenatal benzodiazepine exposure was associated with development of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). DESIGN, SETTING, AND PARTICIPANTS This cohort study used linked data from birth certificate registration and the Taiwan National Health Insurance Research Database from January 1, 2004, to December 31, 2017, on 1 138 732 mothers with 1 516 846 live births between January 1, 2004, and December 31, 2017. Data were analyzed between February 20, 2021, and September 19, 2022. EXPOSURE Benzodiazepine exposure during pregnancy (first trimester to third trimester) was defined as having at least one benzodiazepine prescription dispensed. MAIN OUTCOMES AND MEASURES The main outcomes were ADHD and ASD. RESULTS There were 1 516 846 children (mean [SD] gestational age, 38.5 [1.8] years; 789 455 boys [52.0%]) born full term who were younger than 14 years of age and followed up to 2017; 5.0% of the children (n = 76 411) were exposed to a benzodiazepine during pregnancy. Benzodiazepine exposure during pregnancy was associated with increased risks of ADHD (first trimester exposure: hazard ratio [HR], 1.24 [95% CI, 1.20-1.28]; second trimester exposure: HR, 1.27 [95% CI, 1.21-1.34]; third trimester exposure: HR, 1.25 [95% CI, 1.14-1.37]) and ASD (first trimester exposure: HR, 1.13 [95% CI, 1.05-1.21]; second trimester exposure: HR, 1.10 [95% CI, 0.98-1.22]; third trimester exposure: HR, 1.21 [95% CI, 1.00-1.47]). However, no differences were found with unexposed sibling controls during the same time frame for ADHD (first trimester exposure: HR, 0.91 [95% CI, 0.83-1.00]; second trimester exposure: HR, 0.89 [95% CI, 0.78-1.01]; third trimester exposure: HR, 1.08 [95% CI, 0.83-1.41]) or ASD (first trimester exposure: HR, 0.92 [95% CI, 0.75-1.14]; second trimester exposure: HR, 0.97 [95% CI, 0.71-1.33]; third trimester exposure: HR, 1.07 [95% CI, 0.53-2.16]). Similar findings were also noted in the stratification analysis of short-acting and long-acting benzodiazepines. CONCLUSIONS AND RELEVANCE This cohort study suggests that previously described adverse neurodevelopmental outcomes associated with benzodiazepine exposure during pregnancy were likely to be accounted for by maternal genetic confounding.
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Affiliation(s)
- Vincent Chin-Hung Chen
- Department of Psychiatry, Chiayi Chang Gung Memorial Hospital, Chiayi, Taiwan
- Department of Psychiatry, Chang Gung University, Taoyuan, Taiwan
| | - Shu-I Wu
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
- Department of Psychiatry, Mackay Memorial Hospital, Taipei, Taiwan
| | - Chiao-Fan Lin
- Department of Psychiatry, Chang Gung University, Taoyuan, Taiwan
- Department of Psychiatry, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Mong-Liang Lu
- Department of Psychiatry, Wan-Fang Hospital & School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yi-Lung Chen
- Department of Healthcare Administration, Asia University, Taichung, Taiwan
- Department of Psychology, Asia University, Taichung, Taiwan
| | - Robert Stewart
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kindgom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
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The promise of a model-based psychiatry: building computational models of mental ill health. Lancet Digit Health 2022; 4:e816-e828. [PMID: 36229345 PMCID: PMC9627546 DOI: 10.1016/s2589-7500(22)00152-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/05/2022] [Accepted: 07/27/2022] [Indexed: 11/07/2022]
Abstract
Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.
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Evaluating the tendencies of community practitioners who actively practice in child and adolescent psychiatry to diagnose and treat DSM-5 attenuated psychotic syndrome. Eur Child Adolesc Psychiatry 2022; 31:1635-1644. [PMID: 34669043 DOI: 10.1007/s00787-021-01897-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 10/13/2021] [Indexed: 10/20/2022]
Abstract
The detection of individuals at clinical ultra-high risk for psychosis (CHR-P) may be a key limiting step for early interventions, and there is some uncertainty regarding the true clinical reliability of the CHR-P states. The aim of this study was to explore how practitioners who were in the direct treatment of children with psychiatric disorders [child psychiatry specialists/trainees (n = 227, n = 131), adult psychiatrists (n = 27), and child neurologists (n = 2)] perceive the DSM-5-Attenuated Psychosis Syndrome (DSM-5-APS), and their clinical routine practice in the treatment of it. Three vignettes describing fictional cases presented with symptoms of either DSM-5-Schizophrenia, DSM-5-APS, and no psychotic symptoms were created. We asked these practitioners to apply a DSM-5 diagnosis and to choose appropriate treatment(s) for these vignettes. Of the responders, 43% correctly diagnosed the APS vignette, whereas 37.4% mentioned that it had a full-blown psychotic episode. Regarding the therapeutic approach for the APS vignette, 72.1% of all practitioners chose a psychopharmacological intervention and 32% individual psychotherapy. This study showed that the diagnostic inter-rater reliability of the DSM-5-APS among child/adolescent mental health practitioners was consistent with the results from the DSM-5 field trials (Kappa = 0.46). Moreover, almost three in four practitioners endorsed psychopharmacological intervention as a treatment option for the DSM-5-APS case. The lack of evidence of psychopharmacological interventions in CHR-P situations emphasizes that the least harmful interventions should be recommended. Thus, our findings indicated a need for raising awareness regarding the CHR-P paradigm and its treatment as well as the development of solid guidelines that can be implemented in clinical practice.
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Oskolkova S. Schizophrenia: a Narrative Review of Etiological and Diagnostic Issues. CONSORTIUM PSYCHIATRICUM 2022; 3:19-34. [PMID: 39044913 PMCID: PMC11262116 DOI: 10.17816/cp132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 04/28/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Despite the fact that schizophrenia has already been described historically and researched for a long time, this disorder remains unclear and controversial in many respects, including its etiology, pathogenesis, classification, diagnosis, and therapy. METHODS Literature from the selected sources (elibrary.ru, Russian Science Citation Index and the Russian branch of the Cochrane Library) were searched and analyzed using the diachronic method. Priority was given to reviews, guidelines, and original research on schizophrenia written during the past 10 years. RESULTS Historically, scientists have described schizophrenia as a single disorder, a group of disorders, or even as a combination of certain syndromes. The polymorphic symptoms and the most typical dynamics of various forms of schizophrenia have been systematized, but neither in Russia nor in other countries have the etiology and pathogenesis been proven. The reasons for the under- and overdiagnosis of schizophrenia cannot cover all possible objective and subjective difficulties arising in the diagnostic process. CONCLUSION The existing literature shows that the problem of schizophrenia may not be regarded as settled for a long time. This largely depends on the position of society, the development of biological sciences, and the pathomorphosis of the disorder itself. Many aspects of schizophrenia can become clearer and less controversial with systematic studies based on previous data, as well as data obtained using new research methods.
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Choudhary S, Thomas N, Alshamrani S, Srinivasan G, Ellenberger J, Nawaz U, Cohen R. A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study. JMIR Med Inform 2022; 10:e38943. [PMID: 36040777 PMCID: PMC9472035 DOI: 10.2196/38943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/11/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Anxiety is one of the leading causes of mental health disability around the world. Currently, a majority of the population who experience anxiety go undiagnosed or untreated. New and innovative ways of diagnosing and monitoring anxiety have emerged using smartphone sensor-based monitoring as a metric for the management of anxiety. This is a novel study as it adds to the field of research through the use of nonidentifiable smartphone usage to help detect and monitor anxiety remotely and in a continuous and passive manner. OBJECTIVE This study aims to evaluate the accuracy of a novel mental behavioral profiling metric derived from smartphone usage for the identification and tracking of generalized anxiety disorder (GAD). METHODS Smartphone data and self-reported 7-item GAD anxiety assessments were collected from 229 participants using an Android operating system smartphone in an observational study over an average of 14 days (SD 29.8). A total of 34 features were mined to be constructed as a potential digital phenotyping marker from continuous smartphone usage data. We further analyzed the correlation of these digital behavioral markers against each item of the 7-item Generalized Anxiety Disorder Scale (GAD-7) and its influence on the predictions of machine learning algorithms. RESULTS A total of 229 participants were recruited in this study who had completed the GAD-7 assessment and had at least one set of passive digital data collected within a 24-hour period. The mean GAD-7 score was 11.8 (SD 5.7). Regression modeling was tested against classification modeling and the highest prediction accuracy was achieved from a binary XGBoost classification model (precision of 73%-81%; recall of 68%-87%; F1-score of 71%-79%; accuracy of 76%; area under the curve of 80%). Nonparametric permutation testing with Pearson correlation results indicated that the proposed metric (Mental Health Similarity Score [MHSS]) had a colinear relationship between GAD-7 Items 1, 3 and 7. CONCLUSIONS The proposed MHSS metric demonstrates the feasibility of using passively collected nonintrusive smartphone data and machine learning-based data mining techniques to track an individuals' daily anxiety levels with a 76% accuracy that directly relates to the GAD-7 scale.
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Affiliation(s)
- Soumya Choudhary
- Department of Research, Behavidence, Inc., New York, NY, United States
| | - Nikita Thomas
- Department of Data Science, Behavidence, Inc., New York, NY, United States
| | - Sultan Alshamrani
- Department of Data Science, Behavidence, Inc., New York, NY, United States
| | - Girish Srinivasan
- Department of Data Science, Behavidence, Inc., New York, NY, United States
| | | | - Usman Nawaz
- Department of Data Science, Behavidence, Inc., New York, NY, United States
| | - Roy Cohen
- Department of Research, Behavidence, Inc., New York, NY, United States
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Burden of mental disorders in children in the general population and in health facilities: discrepancies in years lived with disability based on national prevalence estimates between populations receiving care or not. Eur Child Adolesc Psychiatry 2022; 31:1-9. [PMID: 33813661 DOI: 10.1007/s00787-021-01769-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 03/29/2021] [Indexed: 12/27/2022]
Abstract
Little is known about the discrepancies in the burden of child mental disorders based on differences in prevalence between populations with and without care. Identifying such discrepancies may help to elucidate the unmet needs related to child mental disorders. We compared the years lived with disability (YLD) between children with and without care for mental disorders using a representative national survey, Taiwan's National Epidemiological Study of Child Mental Disorders (TNESCMD), and a national health facility database, the Taiwan National Health Insurance Research Database (TNHIRD). The comorbidity-adjusted YLD rate ratio (RR) was reported to quantify the YLD discrepancy. The overall YLD rate for all mental disorders in the TNESCMD was 9.05 times higher than that in the TNHIRD with the lowest and highest YLD RRs for autism spectrum disorder (RR 3.51) and anxiety disorders (RR 360.00). Unlike the YLD proportion explained by attention-deficit/hyperactivity disorder and autism spectrum disorder, the proportions explained by anxiety disorders and conduct disorder/oppositional defiant disorder relative to the total YLD were relatively lower in the TNHIRD than in TNESCMD and the Global Burden of Disease 2016. The discrepancies in YLD between populations with and without care in child mental disorders ranged from ± 55% to 99% and had an overall value of ± 80.1%. High YLD discrepancies in child mental disorders between estimates based on the general population and those in health facilities suggest significant unmet needs for care in child mental disorders and that estimates of disease burden that rely heavily on a single source may result in unreliable results.
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32
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Stolicyn A, Steele JD, Seriès P. Prediction of depression symptoms in individual subjects with face and eye movement tracking. Psychol Med 2022; 52:1784-1792. [PMID: 33161920 DOI: 10.1017/s0033291720003608] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Depression is a challenge to diagnose reliably and the current gold standard for trials of DSM-5 has been in agreement between two or more medical specialists. Research studies aiming to objectively predict depression have typically used brain scanning. Less expensive methods from cognitive neuroscience may allow quicker and more reliable diagnoses, and contribute to reducing the costs of managing the condition. In the current study we aimed to develop a novel inexpensive system for detecting elevated symptoms of depression based on tracking face and eye movements during the performance of cognitive tasks. METHODS In total, 75 participants performed two novel cognitive tasks with verbal affective distraction elements while their face and eye movements were recorded using inexpensive cameras. Data from 48 participants (mean age 25.5 years, standard deviation of 6.1 years, 25 with elevated symptoms of depression) passed quality control and were included in a case-control classification analysis with machine learning. RESULTS Classification accuracy using cross-validation (within-study replication) reached 79% (sensitivity 76%, specificity 82%), when face and eye movement measures were combined. Symptomatic participants were characterised by less intense mouth and eyelid movements during different stages of the two tasks, and by differences in frequencies and durations of fixations on affectively salient distraction words. CONCLUSIONS Elevated symptoms of depression can be detected with face and eye movement tracking during the cognitive performance, with a close to clinically-relevant accuracy (~80%). Future studies should validate these results in larger samples and in clinical populations.
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Affiliation(s)
- Aleks Stolicyn
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5HF, UK
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK
| | - J Douglas Steele
- Division of Imaging Science and Technology, School of Medicine, Dundee University, Ninewells Hospital & Medical School, Dundee DD1 9SY, UK
| | - Peggy Seriès
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK
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Zaitsev OS, Poddubskaya AA, Tomskiy AA, Gamaleya AA, Maksakova OA, Potapov IV, Tsukarzi EE, Mosolov SN. Patients selection for psychiatric neurosurgery: pitfalls and considerations. PROGRESS IN BRAIN RESEARCH 2022; 272:173-183. [PMID: 35667801 DOI: 10.1016/bs.pbr.2022.03.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Neurosurgical interventions (destructive or neuromodulation) are considered as a therapeutic option for patients with treatment resistant mental disorders. However, the issues of indications and contraindications for psychiatric surgery, method and patient selection remain unresolved. This article discusses possible problems and contradictions in the selection of patients, the need for an interdisciplinary team to work to solve the question of the feasibility of using neurosurgical methods. The authors have identified the main problems that increase the risks of selection and lead to a lack of results or low efficiency of neurosurgical intervention, namely: (1) diagnostic errors or inaccuracies; (2) inconclusive data on therapeutic resistance; (3) lack of a common understanding of the goals and desired results among participants in the selection of patients for neurosurgery. Possible predictors of surgical outcome and ethical issues are also discussed. Neurosurgical interventions as a treatment option for psychiatric disorders are not officially approved in most countries. So an appropriate algorithm for patient selection and clear criteria for outcome measures are needed.
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Affiliation(s)
- Oleg S Zaitsev
- Burdenko National Medical Research Center of Neurosurgery, Moscow, Russian Federation.
| | - Anna A Poddubskaya
- Department of Functional Neurosurgery, Burdenko National Medical Research Center of Neurosurgery, Moscow, Russian Federation
| | - Alexey A Tomskiy
- Department of Functional Neurosurgery, Burdenko National Medical Research Center of Neurosurgery, Moscow, Russian Federation
| | - Anna A Gamaleya
- Department of Functional Neurosurgery, Burdenko National Medical Research Center of Neurosurgery, Moscow, Russian Federation
| | - Olga A Maksakova
- Department of Functional Neurosurgery, Burdenko National Medical Research Center of Neurosurgery, Moscow, Russian Federation
| | - Igor V Potapov
- Department of Functional Neurosurgery, Burdenko National Medical Research Center of Neurosurgery, Moscow, Russian Federation
| | - Eduard E Tsukarzi
- Moscow Research Institute of Psychiatry, Serbsky National Medical Research Center of Psychiatry and Narcology, Moscow, Russian Federation
| | - Sergey N Mosolov
- Moscow Research Institute of Psychiatry, Serbsky National Medical Research Center of Psychiatry and Narcology, Moscow, Russian Federation
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Lockhart J, DiCiro M, Rokop J, Brennan A. California Sexually Violent Predator (SVP) Evaluations in the Field: Static-99R and Diagnostic Field Reliability. SEXUAL ABUSE : A JOURNAL OF RESEARCH AND TREATMENT 2022; 34:425-455. [PMID: 34549636 DOI: 10.1177/10790632211042364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Tests and diagnoses used in sexually violent predator (SVP) evaluations must be reliable, as reliability is foundational to validity. The current study contained a stratified sample of evaluations of 395 individuals referred as potential SVPs between 2012 and 2017. Each individual was initially evaluated by at least two experts. The sample included three groups: individuals not meeting SVP criteria (N = 200, or 400 evaluations), individuals meeting SVP criteria (N = 95, with 190 evaluations), and individuals where evaluators disagreed (N = 100, with 200 evaluations). The sample also included 200 subsequent independent evaluations on these "disagree" cases. Static-99R score intraclass coefficient (ICC) interrater reliability was good to excellent within each group and overall. Evaluators scored the Static-99R within one point of each other 87% of the time. Cohen's kappa diagnostic agreement for Pedophilic Disorder was substantial. ASPD and substance abuse kappa were in the "fair" range, while OSPD diagnoses in the positive group were at the "moderate" level of agreement. Ethnic differences in diagnoses were consistent with other studies, with equivalent Static-99R ICC values across ethnic groups. There were no significant differences between state civil servants versus contracted experts in Static-99R ratings or final determinations. The results suggest that Static-99R scores have acceptable reliability in these evaluations, and Pedophilic Disorder (the most common paraphilic disorder in our study) and OSPD can be reliably diagnosed. We discuss limitations of the study, as well as the need for care in high-stakes evaluations given the imperfect reliability of psychological measurements.
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Affiliation(s)
- Joseph Lockhart
- 6475California Department of State Hospitals, Forensic Services Division, Sacramento, CA, USA
| | - Melinda DiCiro
- 6475California Department of State Hospitals, Forensic Services Division, Sacramento, CA, USA
| | - James Rokop
- 6475California Department of State Hospitals, Forensic Services Division, Sacramento, CA, USA
| | - Anna Brennan
- 6475California Department of State Hospitals, Forensic Services Division, Sacramento, CA, USA
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Cautionary Observations Concerning the Introduction of Psychophysiological Biomarkers into Neuropsychiatric Practice. PSYCHIATRY INTERNATIONAL 2022. [DOI: 10.3390/psychiatryint3020015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The combination of statistical learning technologies with large databases of psychophysiological data has appropriately generated enthusiastic interest in future clinical applicability. It is argued here that this enthusiasm should be tempered with the understanding that significant obstacles must be overcome before the systematic introduction of psychophysiological measures into neuropsychiatric practice becomes possible. The objective of this study is to identify challenges to this effort. The nonspecificity of psychophysiological measures complicates their use in diagnosis. Low test-retest reliability complicates use in longitudinal assessment, and quantitative psychophysiological measures can normalize in response to placebo intervention. Ten cautionary observations are introduced and, in some instances, possible directions for remediation are suggested.
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Pan W, Liu C, Zhu D, Liu Y, Mao P, Ren Y, Ma X. Prediction of Antidepressant Efficacy by Cognitive Function in First-Episode Late-Life Depression: A Pilot Study. Front Psychiatry 2022; 13:916041. [PMID: 35669268 PMCID: PMC9163406 DOI: 10.3389/fpsyt.2022.916041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 05/02/2022] [Indexed: 11/17/2022] Open
Abstract
UNLABELLED The response rate of treatment for late-life depression (LLD) is only 25-60%. The cognitive impairment associated with LLD often affects the effectiveness of antidepressants and may has the potential ability to predict response. This study seeks a biomarker for baseline cognitive function to predict efficacy of antidepressants. Sixty patients diagnosed with LLD received escitalopram or sertraline treatment for 8 weeks. Clinical symptom was measured using Hamilton Depression Rating Scale-17 (HAMD-17) and cognitive function was measured using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Trail Making Test (TMT) before and after 8-week treatment. Patients were divided into treatment effective group (TE) and treatment ineffective group (TI) according to reduction rate in scores of HAMD-17 after treatment. Thirty-eight matched healthy controls (HC) were assessed using RBANS and TMT. There was significant decrease of score of RBANS and increase of score of TMT in patients with LLD compared with HC. Regression analysis revealed that change in HAMD-17 score was significantly positively associated with baseline score of picture naming, figure copy, digit span, and delayed memory. The preliminary findings suggested that working memory, attention, visuospatial, language function, and delayed memory should be examined further as a means of providing the useful objective biomarkers of treatment response. CLINICAL TRIALS REGISTRATION [www.ClinicalTrials.gov], identifier [ChiCTR2100042370].
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Affiliation(s)
- Weigang Pan
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Chaomeng Liu
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Dandi Zhu
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yi Liu
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Peixian Mao
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yanping Ren
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xin Ma
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
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Auerbach RP, Pagliaccio D, Hubbard NA, Frosch I, Kremens R, Cosby E, Jones R, Siless V, Lo N, Henin A, Hofmann SG, Gabrieli JDE, Yendiki A, Whitfield-Gabrieli S, Pizzagalli DA. Reward-Related Neural Circuitry in Depressed and Anxious Adolescents: A Human Connectome Project. J Am Acad Child Adolesc Psychiatry 2022; 61:308-320. [PMID: 33965516 PMCID: PMC8643367 DOI: 10.1016/j.jaac.2021.04.014] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 04/17/2021] [Accepted: 04/26/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Although depression and anxiety often have distinct etiologies, they frequently co-occur in adolescence. Recent initiatives have underscored the importance of developing new ways of classifying mental illness based on underlying neural dimensions that cut across traditional diagnostic boundaries. Accordingly, the aim of the study was to clarify reward-related neural circuitry that may characterize depressed-anxious youth. METHOD The Boston Adolescent Neuroimaging of Depression and Anxiety Human Connectome Project tested group differences regarding subcortical volume and nucleus accumbens activation during an incentive processing task among 14- to 17-year-old adolescents presenting with a primary depressive and/or anxiety disorder (n = 129) or no lifetime history of mental disorders (n = 64). In addition, multimodal modeling examined predictors of depression and anxiety symptom change over a 6-month follow-up period. RESULTS Our findings highlighted considerable convergence. Relative to healthy youth, depressed-anxious adolescents exhibited reduced nucleus accumbens volume and activation following reward receipt. These findings remained when removing all medicated participants (∼59% of depressed-anxious youth). Subgroup analyses comparing anxious-only, depressed-anxious, and healthy youth also were largely consistent. Multimodal modeling showed that only structural alterations predicted depressive symptoms over time. CONCLUSION Multimodal findings highlight alterations within nucleus accumbens structure and function that characterize depressed-anxious adolescents. In the current hypothesis-driven analyses, however, only reduced nucleus accumbens volume predicted depressive symptoms over time. An important next step will be to clarify why structural alterations have an impact on reward-related processes and associated symptoms.
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Goodsmith N, Cruz M. Mental Health Services Research and Community Psychiatry. TEXTBOOK OF COMMUNITY PSYCHIATRY 2022:411-425. [DOI: 10.1007/978-3-031-10239-4_30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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O'Connor C, Seery C, Young C. How Does It Feel to Have One's Psychiatric Diagnosis Altered? Exploring Lived Experiences of Diagnostic Shifts in Adult Mental Healthcare. Front Psychiatry 2022; 13:820162. [PMID: 35222120 PMCID: PMC8873081 DOI: 10.3389/fpsyt.2022.820162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 01/18/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Though the socio-emotional significance of psychiatric diagnoses and the frequency of transitions between diagnostic classifications are widely acknowledged, minimal research reveals how "diagnostic shifts" are subjectively experienced by psychiatric service-users. AIM This study investigated how adult service-users make sense of diagnostic shifts and their impacts on one's life. METHODS Twenty-seven people with self-reported experiences of diagnostic shifts opted into this qualitative study. Virtual narrative interviews invited participants to share their "diagnosis stories." Interview transcripts were analyzed using narrative thematic analysis to identify common and divergent experiences across participants. RESULTS Diverse experiences of diagnostic shifts were related: diagnostic shifts could both promote and undermine clinical trust, therapeutic engagement and self-understanding. The analysis suggested that shared and divergent experiences could be attributed to two dimensions of narratives: participants' Interpretations of Diagnostic Shifts and Diagnosis-Specific Factors. Regarding the former, analysis produced a typology of three possible interpretations of diagnostic shifts, which were linked with consistently different antecedents, experiences and consequences. The latter dimension captured how experiences of diagnostic shifts also hinged on the unique meanings ascribed to the specific diagnoses gained and lost, particularly in relation to their perceived severity, stigma, personal associations, and related communities. CONCLUSIONS Findings revealed how diagnostic shifts can be experienced as both traumatic and life-enhancing, depending on their social and subjective context. Understanding the range and predictors of variable experiences of diagnostic shifts is vital for sensitive clinical practice and communication.
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Affiliation(s)
| | - Christina Seery
- School of Psychology, University College Dublin, Dublin, Ireland
| | - Claire Young
- School of Psychology, University College Dublin, Dublin, Ireland
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Gozé T. How to Teach/Learn Praecox Feeling? Through Phenomenology to Medical Education. Front Psychiatry 2022; 13:819305. [PMID: 35370862 PMCID: PMC8971516 DOI: 10.3389/fpsyt.2022.819305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 02/23/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The Praecox Feeling (PF) refers to a classical psychopathological concept describing the specific experience of bizarreness arising in the encounter with a person living with schizophrenia spectrum disorders (SSDs). Some studies have shown that experienced psychiatrists take advantage of this experience to perform accurate and rapid diagnostic expertise. It would seem that PF is not contradictory with an operationalized diagnostic approach, but that the PF would intervene at a more tacit level of medical judgment. However, the articulation between the implicit and explicit levels of the psychiatrist's experience in the situation of medical judgment remains little studied, even though it is of crucial importance for structuring the teaching of clinical psychiatry to mental health practitioners. Can diagnostic intuition be learned? Is this experience a kind of "gift" that some may or may not have? Does the PF refer to medical expertise? METHODS To unfold the complexity of his questions this article proposes to conduct an historical, epistemological and phenomenological analysis of the PF. RESULTS We will first conduct a presentation of historical descriptions of the PF understood as a sensation, intuition and experience, alongside the evolution of the concept of schizophrenia. Then, the article proposes an original phenomenological modelization of the temporal unfolding of the PF. DISCUSSION The phenomenological conceptualization, informed from empirical evidence will try to account for the paradox of the PF as both lived evidence and indescribable experience. PF will be described as a complex cognitive and embodied process based upon ante-predicative aesthetic sensing which is secondly apprehended as perceptible evidence thanks to clinical typification. This conceptualization relying on Husserl manuscript on intersubjectivity will help to demystify its experiential structure and discuss its relevance for medical education.
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Affiliation(s)
- Tudi Gozé
- Department of Psychiatry, Psychotherapies, Art-therapy, Toulouse University Hospital, Toulouse, France.,Equipe de Recherche sur les Rationalités Philosophiques et les Savoirs - EA3051, Université de Toulouse - Jean Jaurès, Toulouse, France
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KATO TADAFUMI. Bipolar Disorder: From Pathophysiology to Treatment. JUNTENDO IJI ZASSHI = JUNTENDO MEDICAL JOURNAL 2021; 68:17-24. [PMID: 38911011 PMCID: PMC11189790 DOI: 10.14789/jmj.jmj21-0026-r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 10/11/2021] [Indexed: 06/25/2024]
Abstract
Bipolar disorder is a mental disorder that involves a manic or hypomanic state and a depressive state, and was once called manic-depressive disorder and was considered one of the two major mental disorders along with schizophrenia. Major depressive disorder, on the other hand, is a disorder in which only depressive states occur, and the two are sometimes referred to together as "mood disorders. This review will introduce the pathophysiology, diagnosis, epidemiology, and treatment of bipolar disorder, focusing on the current situation in Japan.
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Affiliation(s)
- TADAFUMI KATO
- Corresponding author: Tadafumi Kato, Department of Psychiatry & Behavioral Science, Juntendo University Graduate School of Medicine Hongo 2-1-1, Bunkyo, Tokyo 113-8421, Japan TEL: +81-3-5802-1070 FAX: +81-3-5802-1070 E-mail:
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Subramaniapillai M, Chen VCH, McIntyre RS, Yang YH, Chen YL. Added burden of major depressive disorder on cardiovascular morbidity and mortality among patients with cardiovascular disease and the modifying effects of antidepressants: A national retrospective cohort study. J Affect Disord 2021; 294:580-585. [PMID: 34332358 DOI: 10.1016/j.jad.2021.07.082] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 07/13/2021] [Accepted: 07/15/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND To evaluate the likelihood of a future cardiovascular event (i.e., in-hospital mortality or cardiovascular disease [CVD] complications/interventions) among patients with CVD and major depressive disorder (MDD) compared to those without MDD, and the antidepressant use on future cardiovascular events between the two groups. METHODS This is a retrospective cohort with propensity score matching with 8941 patients with CVD and MDD, and 8941 non-MDD patients using data from the Longitudinal Health Insurance Database from 1999 to 2013 in Taiwan. The outcome was in-hospital mortality and the incidence of revascularization (i.e., percutaneous transluminal coronary angioplasty [PTCA] and coronary artery bypass graft surgery [CABG]). RESULTS Patients with CVD and MDD were more likely to need revascularization (an adjusted hazard ratio [aHR]: 1.26 and 95% CI: 1.12-1.43) than those without MDD, regardless of whether PTCA (aHR: 1.23 and 95% CI: 1.07-1.40) or CABG (aHR: 1.60 and 95% CI: 1.16-2.21) had occurred. Antidepressant use was associated with a tendency of reduced risk of mortality (aHR: 0.92 and 95% CI: 0.84-1.00). Although the magnitude of aHR ranged from 0.92 to 0.95 with revascularization, they did not reach significant levels. LIMITATIONS Some covariates could not be controlled because they were not included in the national register dataset, and the causality is limited in an observational study. CONCLUSIONS Patients with CVD with MDD are more likely to experience a cardiovascular complication requiring intervention than CVD patients without MDD. Antidepressant use is associated with reduced in-hospital mortality.
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Affiliation(s)
- Mehala Subramaniapillai
- Mood Disorders Psychopharmacology Unit, Poul Hansen Depression Centre, University Health Network, Toronto, ON, Canada
| | - Vincent Chin-Hung Chen
- School of Medicine, Chang Gung University, Tauyuan, Taiwan; Department of Psychiatry, Chiayi Chang Gung Memorial Hospital, Chiayi, Taiwan.
| | - Roger S McIntyre
- Mood Disorders Psychopharmacology Unit, Poul Hansen Depression Centre, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Brain and Cognition Discovery Foundation, Toronto, ON, Canada; Department of Pharmacology, University of Toronto, Toronto, ON, Canada
| | - Yao-Hsu Yang
- Department of Traditional Chinese Medicine, Chang Gung Memorial Hospital, Chia-Yi, Taiwan; Health Information and Epidemiology Laboratory, Chang Gung Memorial Hospital, Chiayi, Taiwan; School of Traditional Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yi-Lung Chen
- Department of Healthcare Administration, Asia University, Taiwan; Department of Psychology, Asia University, Taiwan.
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Wieckowski AT, de Marchena A, Algur Y, Nichols L, Fernandes S, Thomas RP, McClure LA, Dufek S, Fein D, Adamson LB, Stahmer A, Robins DL. The first five minutes: Initial impressions during autism spectrum disorder diagnostic evaluations in young children. Autism Res 2021; 14:1923-1934. [PMID: 34021728 PMCID: PMC8480227 DOI: 10.1002/aur.2536] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 03/13/2021] [Accepted: 05/04/2021] [Indexed: 11/09/2022]
Abstract
Diagnosticians report that autism spectrum disorder (ASD) is immediately apparent in some, but not all, children ultimately diagnosed. Clinicians' initial diagnostic impressions have implications for ASD early detection, yet the literature raises questions about their accuracy. This study explores diagnostic impressions of ASD specialists made within the first 5 minutes of meeting a young child and investigates factors associated with the match between initial impressions and final diagnoses. Participants were children (n = 294, aged 12-53 months) referred for an ASD evaluation as part of multi-site ASD screening studies. After 5 minutes observing each child, clinicians with expertise diagnosing ASD recorded if they thought the child would meet criteria for ASD following a complete evaluation, and recorded their confidence in this impression. Clinicians' initial impressions matched the final diagnosis in 81% of cases. Ninety-two percent of cases initially thought to have ASD met criteria following a full evaluation; however, 24% of cases initially thought not to have ASD also met criteria, suggesting a high miss rate. Clinicians were generally confident in their initial impressions, reporting highest confidence for children initially thought correctly not to have ASD. ASD behavioral presentation, but not demographic characteristics or developmental level, were associated with matching initial impression and final diagnosis, and confidence. Brief observations indicating ASD should trigger referral to intervention services, but are likely to under-detect positive cases and should not be used to rule out ASD, highlighting the need to incorporate information beyond initial clinical impression. LAY SUMMARY: When children come in for an autism evaluation, clinicians often form early impressions-before doing any formal testing-about whether the child has autism. We studied how often these early impressions match the final diagnosis, and found that clinicians could not easily rule out autism (many children who initially appeared not to have autism were ultimately diagnosed), but were generally accurate ruling in autism (when a child appeared to have autism within 5 minutes, they were almost always so diagnosed).
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Affiliation(s)
| | - Ashley de Marchena
- Department of Behavioral and Social Sciences, University of the Sciences, Philadelphia, Pennsylvania, USA
| | - Yasemin Algur
- Department of Epidemiology & Biostatistics, Drexel University, Philadelphia, Pennsylvania, USA
| | - Lashae Nichols
- A.J. Drexel Autism Institute, Drexel University, Philadelphia, Pennsylvania, USA
| | - Sherira Fernandes
- A.J. Drexel Autism Institute, Drexel University, Philadelphia, Pennsylvania, USA
| | - Rebecca P Thomas
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut, USA
| | - Leslie A McClure
- Department of Epidemiology & Biostatistics, Drexel University, Philadelphia, Pennsylvania, USA
| | - Sarah Dufek
- Department of Psychiatry and Behavioral Sciences, University of California, Davis MIND Institute, Sacramento, California, USA
| | - Deborah Fein
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut, USA
| | - Lauren B Adamson
- Department of Psychology, Georgia State University, Atlanta, Georgia, USA
| | - Aubyn Stahmer
- Department of Psychiatry and Behavioral Sciences, University of California, Davis MIND Institute, Sacramento, California, USA
| | - Diana L Robins
- A.J. Drexel Autism Institute, Drexel University, Philadelphia, Pennsylvania, USA
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Bipolar Depression: A Historical Perspective of the Current Concept, with a Focus on Future Research. Harv Rev Psychiatry 2021; 29:351-360. [PMID: 34310532 DOI: 10.1097/hrp.0000000000000309] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
The aim of this narrative review is to trace the origin of the concept of bipolar depression and to expose some of its limitations. Bipolar depression is a broad clinical construct including experiences ranging from traditional melancholic and psychotic episodes ascribed to "manic-depressive insanity," to another heterogeneous group of depressive episodes originally described in the context of binary models of unipolar depression (e.g., psychogenic depression, neurotic depression). None of the available empirical evidence suggests, however, that these subsets of "bipolar" depression are equivalent in terms of clinical course, disability, family aggregation, and response to treatment, among other relevant diagnostic validators. Therefore, the validity of the current concept of bipolar depression should be a matter of concern. Here, we discuss some of the potential limitations that this broad construct might entail in terms of pathophysiological, clinical, and therapeutic aspects. Finally, we propose a clinical research program for bipolar depression in order to delimit diagnostic entities based on empirical data, with subsequent validation by laboratory or neuroimaging biomarkers. This process will then aid in the development of more specific treatments.
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Screening of Mood Symptoms Using MMPI-2-RF Scales: An Application of Machine Learning Techniques. J Pers Med 2021; 11:jpm11080812. [PMID: 34442456 PMCID: PMC8398545 DOI: 10.3390/jpm11080812] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 08/12/2021] [Accepted: 08/19/2021] [Indexed: 01/01/2023] Open
Abstract
(1) Background: The MMPI-2-RF is the most widely used and most researched test among the tools for assessing psychopathology, and previous studies have established its validity. Mood disorders are the most common mental disorders worldwide; they present difficulties in early detection, go undiagnosed in many cases, and have a poor prognosis. (2) Methods: We analyzed a total of 8645 participants. We used the PHQ-9 to evaluate depressive symptoms and the MDQ to evaluate hypomanic symptoms. We used the 10 MMPI-2 Restructured Form scales and 23 Specific Problems scales for the MMPI-2-RF as predictors. We performed machine learning analysis using the k-nearest neighbor classification, linear discriminant analysis, and random forest classification. (3) Results: Through the machine learning technique, depressive symptoms were predicted with an AUC of 0.634-0.767, and the corresponding value range for hypomanic symptoms was 0.770-0.840. When using RCd to predict depressive symptoms, the AUC was 0.807, but this value was 0.840 when using linear discriminant classification. When predicting hypomanic symptoms with RC9, the AUC was 0.704, but this value was 0.767 when using the linear discriminant method. (4) Conclusions: Using machine learning analysis, we defined that participants' mood symptoms could be classified and predicted better than when using the Restructured Clinical scales.
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Acklin MW, Velasquez JP. Improving Criminal Responsibility Determinations Using Structured Professional Judgment. Front Psychol 2021; 12:700991. [PMID: 34326801 PMCID: PMC8313729 DOI: 10.3389/fpsyg.2021.700991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 06/07/2021] [Indexed: 11/30/2022] Open
Abstract
Forensic psychologists commonly utilize unstructured clinical judgment in aggregating clinical and forensic information in forming opinions. Unstructured clinical judgment is prone to evaluator bias and suboptimal levels of inter-rater reliability. This article proposes Structured Professional Judgment (SPJ) methods as a potential remedy. Following a review of canonical forensic assessment models, the prevalence of bias in forensic judgments, and inter-rater agreement in criminal responsibility (CR) determinations, this article presents a SPJ model for CR evaluations translated from violence risk assessment methodology. A systematic user-friendly methodology is described, applying procedural checklists, application of a mental state at time of the offense (MSO) model using structured data collection methods, aggregation of empirical evidence guidelines, and post-hoc hypothesis testing using the Analysis of Competing Hypotheses (ACH). A case study describes application of the procedural and CR decision model in a complex homicide case. The model demonstrates the power and efficacy of the application of SPJ to forensic decision-making and is relevant to other types of forensic assessment (e.g., competency to stand trial, post-acquittal release decision-making).
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Affiliation(s)
- Marvin W Acklin
- Department of Psychiatry, John A. Burns School of Medicine, University of Hawaii at Mānoa, Honolulu, HI, United States
| | - Joseph P Velasquez
- Department of Psychology, Chaminade University of Honolulu, Honolulu, HI, United States
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Lactobacillus plantarum PS128 and Other Probiotics in Children and Adolescents with Autism Spectrum Disorder: A Real-World Experience. Nutrients 2021; 13:nu13062036. [PMID: 34198499 PMCID: PMC8231766 DOI: 10.3390/nu13062036] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/04/2021] [Accepted: 06/12/2021] [Indexed: 02/07/2023] Open
Abstract
Autism Spectrum Disorder is a neurodevelopmental disorder. Recent data suggest that probiotics can reduce some symptoms of this disorder and Lactobacillus plantarum PS128 has been reported to be especially useful. We recruited a sample of 131 autistic children and adolescents (M:F = 122:19; age: 86.1 ± 41.1 months) and evaluated their changes after use of probiotics by mean of CGI. We found some significant improvements with very few side effects; these positive effects were more evident in younger children. Patients taking Lactobacillus plantarum PS128 had greater improvements and fewer side effects than those taking other probiotics. Our real-life data are consistent with existing literature showing a specific effect of Lactobacillus plantarum PS128 in Autism Spectrum Disorder.
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Kessing LV, González-Pinto A, Fagiolini A, Bechdolf A, Reif A, Yildiz A, Etain B, Henry C, Severus E, Reininghaus EZ, Morken G, Goodwin GM, Scott J, Geddes JR, Rietschel M, Landén M, Manchia M, Bauer M, Martinez-Cengotitabengoa M, Andreassen OA, Ritter P, Kupka R, Licht RW, Nielsen RE, Schulze TG, Hajek T, Lagerberg TV, Bergink V, Vieta E. DSM-5 and ICD-11 criteria for bipolar disorder: Implications for the prevalence of bipolar disorder and validity of the diagnosis - A narrative review from the ECNP bipolar disorders network. Eur Neuropsychopharmacol 2021; 47:54-61. [PMID: 33541809 DOI: 10.1016/j.euroneuro.2021.01.097] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 01/18/2021] [Indexed: 12/16/2022]
Abstract
This narrative review summarizes and discusses the implications of the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 and the upcoming International Classification of Diseases (ICD)-11 classification systems on the prevalence of bipolar disorder and on the validity of the DSM-5 diagnosis of bipolar disorder according to the Robin and Guze criteria of diagnostic validity. Here we review and discuss current data on the prevalence of bipolar disorder diagnosed according to DSM-5 versus DSM-IV, and data on characteristics of bipolar disorder in the two diagnostic systems in relation to extended Robin and Guze criteria: 1) clinical presentation, 2) associations with para-clinical data such as brain imaging and blood-based biomarkers, 3) delimitation from other disorders, 4) associations with family history / genetics, 5) prognosis and long-term follow-up, and 6) treatment effects. The review highlights that few studies have investigated consequences for the prevalence of the diagnosis of bipolar disorder and for the validity of the diagnosis. Findings from these studies suggest a substantial decrease in the point prevalence of a diagnosis of bipolar with DSM-5 compared with DSM-IV, ranging from 30-50%, but a smaller decrease in the prevalence during lifetime, corresponding to a 6% reduction. It is concluded that it is likely that the use of DSM-5 and ICD-11 will result in diagnostic delay and delayed early intervention in bipolar disorder. Finally, we recommend areas for future research.
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Affiliation(s)
- Lars Vedel Kessing
- Copenhagen Affective Disorder research Centre (CADIC), Psychiatric Centre Copenhagen, Department O, University Hospital of Copenhagen, Rigshospitalet, and University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen, Denmark.
| | - Ana González-Pinto
- Department of Psychiatry, BIOARABA, Hospital Universitario de Alava, UPV/EHU. CIBERSAM, Vitoria, Spain
| | - Andrea Fagiolini
- Department of Mental Health and Sensory Organs, University of Siena School of Medicine, Siena, Italy
| | - Andreas Bechdolf
- Department of Psychiatry, Psychotherapy and Psychosomatics, Vivantes Hospital am Urban and Vivantes Hospital im Friedrichshain/Charite Medicine Berlin and University of Cologne, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Ayşegül Yildiz
- Department of Psychiatry, Dokuz Eylül University, İzmir, Turkey
| | - Bruno Etain
- Université de Paris and INSERM UMRS 1144, Paris, France
| | - Chantal Henry
- Department of Psychiatry, Service Hospitalo-Universitaire, GHU Paris Psychiatrie & Neuroscience, Paris, France
| | - Emanuel Severus
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Eva Z Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Gunnar Morken
- Department of Psychiatry, St Olav University Hospital & Department of Mental Health, Norwegian University of Science and Technology - NTNU, Trondheim, Norway
| | - Guy M Goodwin
- Department of Psychiatry, University of Oxford and Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Jan Scott
- Institute of Neuroscience, Newcastle University, Newcastle, United Kingdom
| | - John R Geddes
- Department of Psychiatry, University of Oxford and Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Mikael Landén
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italia; Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Monica Martinez-Cengotitabengoa
- Osakidetza, Basque Health Service. Bioaraba, Health Research Institute, University of the Basque Country, UPV/EHU, Spain; Psychology Clinic of East Anglia. 68 Bishopgate, NR1 4AA, Norwich, United Kingdom
| | - Ole A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Philipp Ritter
- Department of Psychiatry, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany
| | - Ralph Kupka
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Rasmus W Licht
- Aalborg University Hospital, Psychiatry, Aalborg, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - René Ernst Nielsen
- Aalborg University Hospital, Psychiatry, Aalborg, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Thomas G Schulze
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Germany
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada; National Institute of Mental Health, Klecany, Czech Republic
| | - Trine Vik Lagerberg
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Veerle Bergink
- Department of Psychiatry and Department of Obstetrics, Gynecology and Reproductive Science, Icahn School of Medicine and Mount Sinai, New York, USA; Department of Psychiatry, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Eduard Vieta
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
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Kim YK, Park SC. An alternative approach to future diagnostic standards for major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021; 105:110133. [PMID: 33049324 DOI: 10.1016/j.pnpbp.2020.110133] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 10/06/2020] [Indexed: 12/17/2022]
Abstract
During the period extending from 1780 to 1880, the conceptualization of melancholia changed from an intellectual to a mood model. The modern view of depression, based on Kraepelinian dualism, has reflected changes in opinion on psychiatric taxonomy of individual melancholia. From the point of view of an "operational revolution," the diagnostic criteria for major depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders, 3rd edition (DSM-III) were based on a neoKraepelinian approach rooted in disease essentialism. In the revision process from the DSM-IV to the DSM-5, a combined dimensional and categorial approach was used. In the DSM-5, the diagnostic criteria for major depressive disorder are polythetic and operational in approach reflecting the heterogeneity of major depressive disorder. Although 227 different symptom combinations fulfilling the diagnostic criteria for major depressive disorder can be theoretically calculated, certain symptom combinations are more prevalent than others in real clinical situations. The heterogeneity of these operational criteria for major depressive disorder have been criticized in a manner informed by the Wittgensteinian analogy of the language game. Herein, our network analysis proposes a novel perspective on the psychopathology of major depressive disorder. The novel approach suggested here may lay the foundation for a redefinition of the traditional taxonomy of depression.
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Affiliation(s)
- Yong-Ku Kim
- Department of Psychiatry, Korea University Ansan Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Seon-Cheol Park
- Department of Psychiatry, Inje University Haeundae Paik Hospital, Busan, Republic of Korea.
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Obsessive-Compulsive Personality Disorder Co-occurring in Individuals with Obsessive-Compulsive Disorder: A Systematic Review and Meta-analysis. Harv Rev Psychiatry 2021; 29:95-107. [PMID: 33666394 DOI: 10.1097/hrp.0000000000000287] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
LEARNING OBJECTIVES After participating in this activity, learners should be better able to:• Assess the rates of co-occurring obsessive-compulsive personality disorder (OCPD) in patients with obsessive-compulsive disorder (OCD)• Identify characteristics related to OCD with co-occurring OCPD. ABSTRACT The current literature discloses discrepant findings regarding the rates of co-occurring obsessive-compulsive personality disorder (OCPD) in patients with obsessive-compulsive disorder (OCD). In addition, it is not clear which characteristics are related specifically to OCD with co-occurring OCPD. We conducted the first systematic review and meta-analysis of the studies of the prevalence of OCPD in patients with OCD. We also investigated potential moderators of the prevalence, including OCD severity, age of onset of OCD, sex, current age, methodological quality, and publication date of the studies. Electronic databases and gray literature were searched by two independent reviewers. A PRISMA systematic review with a random-effect meta-analysis was conducted. Thirty-four studies were included. A significant mean effect size of 0.25 without publication bias indicated that OCPD was present in 25% of patients with OCD, suggesting that the two conditions are distinct clinical entities. This prevalence was higher than the rates found in the literature for any other personality disorders among OCD patients. OCPD that occurs in the context of OCD was more likely to be present in males and to be characterized by a later age of onset of OCD, older age at assessment, and less severe OCD symptoms. Clinicians should consider these findings when assessing and planning treatment of OCD with co-occurring OCPD.
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