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Treatment response biomarkers in anxiety disorders: From neuroimaging to neuronally-derived extracellular vesicles and beyond

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Biomarkers in Neuropsychiatry 2020年 3卷 100024-100024页

作者： Strawn, Jeffrey R. Levine, Amir Department of Psychiatry and Behavioral Neuroscience Anxiety Disorders Research Program College of Medicine University of Cincinnati Cincinnati OH United States Department of Pediatrics Division of Child & Adolescent Psychiatry and Division of Clinical Pharmacology Cincinnati Children's Hospital Medical Center Cincinnati OH United States Department of Psychiatry Columbia University and New York State Psychiatric Institute New York NY United States

Multiple and diverse psychotherapeutic or psychopharmacologic treatments effectively reduce symptoms for many patients with anxiety disorders, but the trajectory and magnitude of response vary considerably. This heterogeneity of treatment response has invigorated the search for biomarkers of treatment response in anxiety disorders, across the lifespan. In this review, we summarize evidence for biomarkers of treatment response in children, adolescents and adults with generalized, separation and social anxiety disorders as well as panic disorder. We then discuss the relationship between these biomarkers of treatment response and the pathophysiology of anxiety disorders. Finally, we provide context for treatment response biomarkers of the future, including neuronally-derived extracellular vesicles in anxiety disorders and discuss challenges that must be overcome prior to the debut of treatment response biomarkers in the clinic. A number of promising treatment response biomarkers have been identified, although there is an urgent need to replicate findings and to identify which biomarkers might guide clinicians in selecting from available treatments rather than just simply identifying patients who may be less likely to respond to a given intervention. © 2020 The Author(s)

关键词： fMRI Generalized anxiety disorder Pharmacogenomics Separation anxiety disorder

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Enhancing Discovery of Genetic Variants for Posttraumatic Stress Disorder Through Integration of Quantitative Phenotypes and Trauma Exposure Information (vol 91, pg 626, 2022)

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BIOLOGICAL PSYCHIATRY 2022年第7期91卷 690-690页

作者： Maihofer, Adam X. Vinkers, Christiaan H. Department of Psychiatry University of California San Diego La Jolla California Department of Family Medicine and Public Health University of California San Diego La Jolla California Center of Excellence for Stress and Mental Health Veterans Affairs Healthcare System San Diego California. Electronic address: amaihofer@health.ucsd.edu. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts Department of Psychiatry Massachusetts General Hospital Boston Massachusetts. Social Genetic and Developmental Psychiatry Centre Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom National Institute of Health Research Maudsley Biomedical Research Centre King's College London London United Kingdom. Department of Psychiatry Harvard Medical School Boston Massachusetts Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge Massachusetts Center of Excellence in Depression and Anxiety Disorders Belmont Massachusetts. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge Massachusetts. Department of Psychiatry University of California San Diego La Jolla California Center of Excellence for Stress and Mental Health Veterans Affairs Healthcare System San Diego California Research Service Veterans Affairs Healthcare System San Diego California. Duke Molecular Physiology Institute Duke University School of Medicine Durham North Carolina. Department of Psychiatry Veterans Affairs Connecticut Healthcare System West Haven Connecticut Department of Psychiatry Yale University School of Medicine New Haven Connecticut. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts Department of Epidemiology Columbia University Mailman School of Public Health New York. Division of Mental Health Atlanta Veterans Affairs Medical Center

BACKGROUND: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). METHODS: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran program GWAS of PTSD total symptoms. RESULTS: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran program. CONCLUSIONS: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods.

关键词： GWAS Genetics Heritability PTSD PheWAS Trauma

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Correction to: Effects of low-intensity training on the brain and muscle in the congenital muscular dystrophy 1D model

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Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology 2022年第11期43卷 6613页

作者： Clarissa M Comim Jaime A Soares Adriano Alberti Viviane Freiberger Letícia Ventura Paula Dias Aryadnne L Schactae Leoberto R Grigollo Amanda V Steckert Daniel F Martins Rudy J Nodari Junior Mariz Vainzof João Quevedo Research Group in Neurodevelopment of Childhood and Adolescence Laboratory of Experimental Neuroscience Postgraduate Program in Health Sciences University of South Santa Catarina (UNISUL) Avenida Pedra Branca 25 Pedra Branca Palhoça SC 88137‑270 Brazil. Research Group in Neurodevelopment of Childhood and Adolescence Laboratory of Experimental Neuroscience Postgraduate Program in Health Sciences University of South Santa Catarina (UNISUL) Avenida Pedra Branca 25 Pedra Branca Palhoça SC 88137‑270 Brazil. adrianoalberti90@***. Translational Psychiatry Laboratory Graduate Program in Health Sciences University of Southern Santa Catarina (UNESC) Criciúma SC Brazil. Research Department Salus Dermatoglifia Luzerna 89609‑000 Santa Catarina Brazil. Human Genome Research Center Biosciences Institute University of São Paulo São Paulo Brazil. Center of Excellence On Mood Disorders Department of Psychiatry and Behavioral Sciences McGovern Medical School The University of Texas Health Science Center at Houston (UTHealth) Houston TX USA. Neuroscience Graduate Program The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Houston TX USA. Translational Psychiatry Program Department of Psychiatry and Behavioral Sciences McGovern Medical School The University of Texas Health Science Center at Houston (UTHealth) Houston TX USA.

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Corrigendum to "Resilience and mental health during the COVID-19 pandemic" [J. Affect. Disord. 2021 Mar 15;283:156-164]

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Journal of affective disorders 2023年 333卷 572页

作者： N Verdolini S Amoretti L Montejo C García-Rizo B Hogg G Mezquida F D Rabelo-da-Ponte C Vallespir J Radua A Martinez-Aran I Pacchiarotti A R Rosa M Bernardo E Vieta C Torrent B Solé Bipolar and Depressive Disorders Unit Hospital Clinic University of Barcelona Institute of Neuroscience IDIBAPS CIBERSAM 170 Villarroel St. 08037 Barcelona Catalonia Spain. Bipolar and Depressive Disorders Unit Hospital Clinic University of Barcelona Institute of Neuroscience IDIBAPS CIBERSAM 170 Villarroel St. 08037 Barcelona Catalonia Spain Barcelona Clinic Schizophrenia Unit Hospital Clinic of Barcelona Department of Medicine Institut de Neurociències Universitat de Barcelona IDIBAPS CIBERSAM 170 Villarroel St. 08037 Barcelona Spain. Centre Fórum Research Unit Parc de Salut Mar 410 Llull St. 08019 Barcelona Catalonia Spain Hospital del Mar Medical Research Institute (IMIM) 88 Dr. Aiguader St. 08003 Barcelona Catalonia Spain Predoctoral Program Department of Psychiatry and Forensic Medicine Universitat Autònoma de Barcelona (UAB) UAB Campus Plaça Cívica 08193 Bellaterra Barcelona Spain. Laboratory of Molecular Psychiatry Hospital de Clínicas de Porto Alegre (HCPA) 2350 Ramiro Barcelos St. 211 Protásio Alves Av. Santa Cecilia Porto Alegre RS 90035-903 Brazil Department of Pharmacology Health Science Basic Institute Postgraduate Program in Psychiatry and Behavioral Sciences and Postgraduate Program of Pharmacology and Therapeutics Universidade Federal do Rio Grande do Sul (UFRGS) 110 Paulo Gama Av. Farroupilha Porto Alegre RS 90040-060 Brazil. Imaging of Mood- and Anxiety-Related Disorders (IMARD) group IDIBAPS CIBERSAM Barcelona Catalonia Spain Early Psychosis: Interventions and Clinical-detection (EPIC) lab Department of Psychosis Studies Institute of Psychiatry Psychology & Neuroscience King's College London London UK Centre for Psychiatric Research and Education Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden. Bipolar and Depressive Disorders Unit Hospital Clinic University of Barcelona Institute of Neuroscience IDIBAPS CIBERSAM 170 Villarroel St. 08037 Barcelona Catalonia Spain. Electronic a

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Atlas of Gray Matter Volume Differences Across Psychiatric Conditions: A Systematic Review With a Novel Meta-Analysis That Considers Co-Occurring disorders

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Biological Psychiatry 2025年

作者： Fortea, Lydia Ortuño, Maria De Prisco, Michele Oliva, Vincenzo Albajes-Eizagirre, Anton Fortea, Adriana Madero, Santiago Solanes, Aleix Vilajosana, Enric Yao, Yuanwei Del Fabro, Lorenzo Solé, Eduard Verdolini, Norma Farré-Colomés, Alvar Serra-Blasco, Maria Picó-Pérez, Maria Lukito, Steve Wise, Toby Carlisi, Christina Arnone, Danilo Kempton, Matthew J. Hauson, Alexander Omar Wollman, Scott Soriano-Mas, Carles Rubia, Katya Norman, Luke Fusar-Poli, Paolo Mataix-Cols, David Valentí, Marc Via, Esther Cardoner, Narcis Solmi, Marco Zhang, Jintao Pan, Pinglei Shin, Jae Il Fullana, Miquel A. Vieta, Eduard Radua, Joaquim Institut d'Investigacions Biomèdiques August Pi I Sunyer Barcelona Spain Department of Medicine University of Barcelona Institute of Neuroscience Barcelona Spain Centro de Investigación Biomédica en Red de Salud Mental Instituto de Salud Carlos III Madrid Spain Bipolar and Depressive Disorders Unit Hospital Clínic Barcelona Spain Department of Biomedical and Neuromotor Sciences University of Bologna Bologna Italy Psychiatric and Psychology Service Hospital Clínic Barcelona Spain Schizophrenia Unit Hospital Clínic Barcelona Spain Department of Psychology The University of Hong Kong Hong Kong Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Pathophysiology and Transplantation University of Milan Milan Italy Local Health Unit Umbria 1 Department of Mental Health Mental Health Center of Perugia Perugia Italy Department of Addictive Behaviour and Addiction Medicine Central Institute of Mental Health Medical Faculty of Mannheim Heidelberg University Mannheim Germany eHealth ICOnnecta't Program and Psycho-Oncology Service Institut Català d'Oncologia L'Hospitalet de Llobregat Spain Psycho-oncology and Digital Health Group Health Services Research in Cancer Institut d'Investigació Biomèdica de Bellvitge L'Hospitalet del Llobregat Spain Live and Health Sciences Research Institute University of Minho Braga Portugal ICVS/3B's PT Government Associate Laboratory Guimarães Braga Portugal Departamento de Psicología Básica Universitat Jaume I Castelló de la Plana Spain Department of Child and Adolescent Psychiatry Institute of Psychology Psychiatry and Neuroscience King's College London London United Kingdom Department of Neuroimaging Institute of Psychiatry Psychology & Neurosciences King's College London London United Kingdom Division of Psychology and Language Sciences University College London London United Kingdom Centre for Affective Disorders Psychological Medicine King's College London Londo

Background: Regional gray matter volume (GMV) differences between individuals with mental disorders and comparison participants may be confounded by co-occurring disorders. To disentangle disorder-specific GMV correlates, we conducted a large-scale multidisorder meta-analysis using a novel approach that explicitly models co-occurring disorders. Methods: We systematically reviewed voxel-based morphometry studies indexed in PubMed and Scopus up to January 2023 that compared adults with major mental disorders (anorexia nervosa, schizophrenia spectrum, anxiety, bipolar, major depressive, obsessive-compulsive, and posttraumatic stress disorders plus attention-deficit/hyperactivity, autism spectrum, and borderline personality disorders) with comparison participants. Two authors independently extracted data and assessed quality using the Newcastle-Ottawa Scale. We derived GMV correlates for each disorder using: 1) a multidisorder meta-analysis that accounted for all co-occurring mental disorders simultaneously and 2) separate standard meta-analyses for each disorder in which co-occurring disorders were ignored. We assessed the alterations’ extent, intensity (effect size), and specificity (interdisorder correlations and transdiagnostic alterations) for both approaches. Results: We included 433 studies (499 datasets) involving 19,718 patients and 16,441 comparison participants (51% female, ages 20–67 years). We provide GMV correlate maps for each disorder using both approaches. The novel approach, which accounted for co-occurring disorders, produced GMV correlates that were more focal and disorder specific (less correlated across disorders and fewer transdiagnostic abnormalities). Conclusions: This work offers the most comprehensive atlas of GMV correlates across major mental disorders. Modeling co-occurring disorders yielded more specific correlates, supporting this approach's validity. The atlas NIfTI maps are available online. © 2024 Society of Biological Psychiatry

关键词： Anxiety disorders Comorbidity Gray matter volume Major depressive disorder Meta-analysis Psychiatric conditions

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Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model

arXiv

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arXiv 2023年

作者： Goya-Maldonado, Roberto Erwin-Grabner, Tracy Zeng, Ling-Li Ching, Christopher R.K. Aleman, Andre Amod, Alyssa R. Basgoze, Zeynep Benedetti, Francesco Besteher, Bianca Brosch, Katharina Bülow, Robin Colle, Romain Connolly, Colm G. Corruble, Emmanuelle Couvy-Duchesne, Baptiste Cullen, Kathryn Dannlowski, Udo Davey, Christopher G. Dols, Annemiek Ernsting, Jan Evans, Jennifer W. Fisch, Lukas Fuentes-Claramonte, Paola Gonul, Ali Saffet Gotlib, Ian H. Grabe, Hans J. Groenewold, Nynke A. Grotegerd, Dominik Hahn, Tim Hamilton, J. Paul Han, Laura K.M. Harrison, Ben J. Ho, Tiffany C. Jahanshad, Neda Jamieson, Alec J. Karuk, Andriana Kircher, Tilo Klimes-Dougan, Bonnie Koopowitz, Sheri-Michelle Lancaster, Thomas Leenings, Ramona Li, Meng Linden, David E.J. MacMaster, Frank P. Mehler, David M.A. Meinert, Susanne Melloni, Elisa Mueller, Bryon A. Mwangi, Benson Nenadić, Igor Ojha, Amar Okamoto, Yasumasa Oudega, Mardien L. Penninx, Brenda W.J.H. Poletti, Sara Pomarol-Clotet, Edith Portella, Maria J. Pozzi, Elena Radua, Joaquim Rodríguez-Cano, Elena Sacchet, Matthew D. Salvador, Raymond Schrantee, Anouk Sim, Kang Soares, Jair C. Solanes, Aleix Stein, Dan J. Stein, Frederike Stolicyn, Aleks Thomopoulos, Sophia I. Toenders, Yara J. Uyar-Demir, Aslihan Vieta, Eduard Vives-Gilabert, Yolanda Völzke, Henry Walter, Martin Whalley, Heather C. Whittle, Sarah Winter, Nils Wittfeld, Katharina Wright, Margaret J. Wu, Mon-Ju Yang, Tony T. Zarate, Carlos Veltman, Dick J. Schmaal, Lianne Thompson, Paul M. Georg-August University Göttingen Germany College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China Imaging Genetics Center Mark & Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Marina del ReyCA90274 United States Department of Biomedical Sciences of Cells and Systems University Medical Center Groningen University of Groningen Groningen Netherlands Department of Psychiatry & Mental Health Neuroscience Institute University of Cape Town Cape Town South Africa Department of Psychiatry and Behavioral Science University of Minnesota Medical School MinneapolisMN United States Division of Neuroscience IRCCS Scientific Institute Ospedale San Raffaele Milano Italy Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany Department of Psychiatry and Psychotherapy University of Marburg Rudolf Bultmann Str. 8 Marburg35039 Germany Institute for Radiology and Neuroradiology University Medicine Greifswald Greifswald Germany MOODS Team CESP INSERM U1018 Faculté de Médecine Univ Paris-Saclay Le Kremlin Bicêtre94275 France Service Hospitalo Universitaire de Psychiatrie de Bicêtre Hôpitaux Universitaires Paris-Saclay Assistance Publique-Hôpitaux deParis Hôpital de Bicêtre Le Kremlin BicêtreF-94275 France Department of Biomedical Sciences Florida State University TallahasseeFL United States Sorbonne University Paris Brain Institute - ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié Salpêtrière ParisF-75013 France Institute for Molecular Bioscience the University of Queensland St LuciaQLD Australia Institute for Translational Psychiatry University of Münster Münster Germany Melbourne Neuropsychiatry Centre Department of Psychiatry the University of Melbourne ParkvilleVIC Australia Department of Psychiatry Amsterdam UMC Vrije Universiteit Amsterdam Amsterdam Neuroscience Amsterdam Public Health Research Institute Am

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing 7,012 participants from 30 sites (N=2,772 MDD and N=4,240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%;SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%;SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible. Future studies are needed to determine whether more sophisticated integration of information from ot

关键词： Support vector machines

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In Addition to Stigma: Cognitive and Autism-Related Predictors of Mental Health in Transgender Adolescents

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Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53 2023年第2期52卷 212-229页

作者： John F Strang Laura G Anthony Amber Song Meng-Chuan Lai Megan Knauss Eleonora Sadikova Elizabeth Graham Zosia Zaks Harriette Wimms Laura Willing David Call Michael Mancilla Sara Shakin Eric Vilain Da-Young Kim Tekla Maisashvili Ayesha Khawaja Lauren Kenworthy Gender Development Program Children's National Hospital. Center for Autism Spectrum Disorders Division of Neuropsychology Children's National Hospital. Center for Neuroscience Children's National Research Institute Children's National Hospital. Departments of Pediatrics Psychiatry and Behavioral Sciences George Washington University School of Medicine. Department of Psychiatry University of Colorado School of Medicine. Pediatric Mental Health Institute Children's Hospital of Colorado. The Margaret and Wallace McCain Centre for Child Youth & Family Mental Health Azrieli Adult Neurodevelopmental Centre and Campbell Family Mental Health Research Institute Centre for Addiction and Mental Health. Department of Psychiatry The Hospital for Sick Children. Department of Psychiatry Temerty Faculty of Medicine University of Toronto. Autism Research Centre Department of Psychiatry University of Cambridge. Department of Psychiatry National Taiwan University Hospital and College of Medicine. Alliance of Community Health Plans. Curry School of Education University of Virginia. The Arc of the United States National Council of Self-Advocates. Hussman Center for Adults with Autism Towson University. Youth Gender Care Services The Village Family Support Center of Baltimore. Youth Pride Clinic Adolescent and Young Adult Medicine Children's National Hospital. Department of Pediatrics Sinai Hospital of Baltimore. Center for Genetic Medicine Research Children's National Hospital. Department of Genomics and Precision Medicine George Washington University. Epigenetics Data & Politics at Centre National de la Recherche Scientifique.

OBJECTIVE:Autism spectrum disorder (ASD) is significantly over-represented among transgender adolescents. Independently, ASD and gender diversity are associated with increased mental health risks. Yet, mental health in autistic-transgender adolescents is poorly understood. This study investigates mental health in the largest matched sample to date of autistic-transgender, non-autistic (allistic) transgender, and autistic-cisgender adolescents diagnosed using gold-standard ASD diagnostic procedures. In accordance with advancing understanding of sex/gender-related autism phenotypes, slightly subthreshold autistic diagnostic presentations (common in autistic girls/women) are modeled. METHOD:This study includes 93 adolescents aged 13-21, evenly divided between autistic-transgender, autistic-cisgender, and allistic-transgender groups; 13 transgender adolescents were at the margin of ASD diagnosis and included within a larger "broad-ASD" grouping. Psychological and neuropsychological evaluation included assessment of mental health, IQ, LGBT stigma, ASD-related social symptoms, executive functioning (EF), and EF-related barriers to achieving gender-related needs. RESULTS:Autistic-transgender adolescents experienced significantly greater internalizing symptoms compared to allistic-transgender and autistic-cisgender groups. In addition to stigma-related associations with mental health, ASD-related cognitive/neurodevelopmental factors (i.e., poorer EF and greater social symptoms) were associated with worse mental health: specifically, social symptoms and EF gender barriers with greater internalizing and EF problems and EF gender barriers with greater suicidality. Comparing across all ASD and gender-related groups, female gender identity was associated with greater suicidality. CONCLUSIONS:Parsing the heterogeneity of mental health risks among transgender youth is critical for developing targeted assessments and interventions. This study identifies ASD diagnosis, ASD phenotypi

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Corrigendum to "Effects of the COVID-19 pandemic and lockdown in Spain: Comparison between community controls and patients with a psychiatric disorder. Preliminary results from the BRIS-MHC STUDY" [J. Affect Disord. 2021 Feb 15;281:13-23]

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Journal of affective disorders 2023年 333卷 571页

作者： B Solé N Verdolini S Amoretti L Montejo A R Rosa B Hogg C Garcia-Rizo G Mezquida M Bernardo A Martinez-Aran E Vieta C Torrent Bipolar and Depressive Disorders Unit Hospital Clinic Institute of Neurosciences University of Barcelona IDIBAPS CIBERSAM Barcelona Catalonia Spain Barcelona Clinic Schizophrenia Unit Hospital Clinic of Barcelona August Pi I Sunyer Biomedical Research Institute (IDIBAPS) CIBERSAM Neuroscience Institute University of Barcelona Spain. Laboratory of Molecular Psychiatry Hospital de Clínicas de Porto Alegre (HCPA) Porto Alegre RS Brazil Department of Pharmacology Postgraduate Program in Psychiatry and Behavioral Sciences and Postgraduate Program of Pharmacology and Therapeutics Universidade Federal do Rio Grande do Sul (UFRGS) Porto Alegre RS Brazil. Centre Fórum Research Unit Parc de Salut Mar Barcelona Spain Hospital del Mar Medical Research Institute (IMIM) Barcelona Spain Predoctoral program Department of Psychiatry and Forensic Medicine Universitat Autònoma de Barcelona (UAB) Spain. Barcelona Clinic Schizophrenia Unit Hospital Clinic of Barcelona August Pi I Sunyer Biomedical Research Institute (IDIBAPS) CIBERSAM Neuroscience Institute University of Barcelona Spain. Electronic address: evieta@clinic.cat.

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Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

arXiv

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arXiv 2022年

作者： Belov, Vladimir Erwin-Grabner, Tracy Gonul, Ali Saffet Amod, Alyssa R. Ojha, Amar Aleman, Andre Dols, Annemiek Schrantee, Anouk Uyar-Demir, Aslihan Harrison, Ben J. Mwangi, Benson Besteher, Bianca Klimes-Dougan, Bonnie Penninx, Brenda W.J.H. Mueller, Bryon A. Zarate, Carlos Davey, Christopher G. Ching, Christopher R.K. Connolly, Colm G. Fu, Cynthia H.Y. Stein, Dan J. Dima, Danai Linden, David E.J. Mehler, David M.A. Pomarol-Clotet, Edith Pozzi, Elena Melloni, Elisa Benedetti, Francesco MacMaster, Frank P. Grabe, Hans J. Völzke, Henry Gotlib, Ian H. Soares, Jair C. Evans, Jennifer W. Sim, Kang Wittfeld, Katharina Cullen, Kathryn Reneman, Liesbeth Oudega, Mardien L. Wright, Margaret J. Portella, Maria J. Sacchet, Matthew D. Li, Meng Aghajani, Moji Wu, Mon-Ju Jaworska, Natalia Jahanshad, Neda van der Wee, Nic J.A. Groenewold, Nynke Hamilton, Paul J. Sämann, Philipp G. Bülow, Robin Poletti, Sara Whittle, Sarah Thomopoulos, Sophia I. van der Werff, Steven J.A. Koopowitz, Sheri-Michelle Lancaster, Thomas Ho, Tiffany C. Yang, Tony T. Basgoze, Zeynep Veltman, Dick J. Schmaal, Lianne Thompson, Paul M. Goya-Maldonado, Roberto Georg-August University Von-Siebold-Str. 5 Goettingen37075 Germany SoCAT Lab Department of Psychiatry School of Medicine Ege University Izmir Turkey Department of Psychiatry & Mental Health University of Cape Town Cape Town South Africa Center for Neuroscience University of Pittsburgh PittsburghPA United States Center for Neural Basis of Cognition University of Pittsburgh PittsburghPA United States Department of Biomedical Sciences of Cells and Systems University Medical Center Groningen University of Groningen Groningen Netherlands Department of Psychiatry Amsterdam UMC Vrije Universiteit Amsterdam Amsterdam Neuroscience Amsterdam Public Health Research Institute Amsterdam Netherlands Amsterdam University Medical Centers Location AMC Department of Radiology and Nuclear Medicine Amsterdam Netherlands Melbourne Neuropsychiatry Centre Department of Psychiatry The University of Melbourne ParkvilleVIC Australia Louis A. Faillace MD Department of Psychiatry and Behavioral Sciences The University of Texas Health Science Center at Houston HoustonTX United States Center of Excellence On Mood Disorders Louis A. Faillace MD Department of Psychiatry and Behavioral Sciences McGovern Medical School The University of Texas Health Science Center HoustonTX United States Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany Department of Psychology University of Minnesota MinneapolisMN United States Department of Psychiatry and Behavioral Science University of Minnesota Medical School MinneapolisMN United States Section on the Neurobiology and Treatment of Mood Disorders National Institute of Mental Health BethesdaMD United States Imaging Genetics Center Mark & Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Marina del ReyCA90274 United States Department of Biomedical Sciences Florida Stateu University TallahasseeFL United States School of Psychology University

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (n=5,356) to provide a generalizable ML classification benchmark of major depressive disorder (MDD). Using brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD vs healthy controls (HC) with around 62% balanced accuracy, but when harmonizing the data using ComBat balanced accuracy dropped to approximately 52%. Similar results were observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may achieve more encouraging prospects. © 2022, CC BY-NC-ND.

关键词： Brain mapping

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Global cross-cultural validation of a brief measure for identifying potential suicide risk in 42 countries

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Public Health 2024年 229卷 13-23页

作者： Gewirtz-Meydan, A. Koós, M. Nagy, L. Kraus, S.W. Demetrovics, Z. Potenza, M.N. Ballester-Arnal, R. Batthyány, D. Bergeron, S. Billieux, J. Burkauskas, J. Cárdenas-López, G. Carvalho, J. Castro-Calvo, J. Chen, L. Ciocca, G. Corazza, O. Csako, R. Fernandez, D.P. Fujiwara, H. Fernandez, E.F. Fuss, J. Gabrhelík, R. Gjoneska, B. Gola, M. Grubbs, J.B. Hashim, H.T. Islam, M.S. Ismail, M. Jiménez-Martínez, M.C. Jurin, T. Kalina, O. Klein, V. Költő, A. Lee, S.-K. Lewczuk, K. Lin, C.-Y. Lochner, C. López-Alvarado, S. Lukavská, K. Mayta-Tristán, P. Miller, D.J. Orosová, O. Orosz, G. Ponce, F.P. Quintana, G.R. Quintero Garzola, G.C. Ramos-Diaz, J. Rigaud, K. Rousseau, A. De Tubino Scanavino, M. Schulmeyer, M.K. Sharan, P. Shibata, M. Shoib, S. Sigre-Leirós, V. Sniewski, L. Spasovski, O. Steibliene, V. Stein, D.J. Strong, C. Ünsal, B.C. Vaillancourt-Morel, M.-P. Van Hout, M.C. Bőthe, B. School of Social Work Faculty of Social Welfare and Health Sciences University of Haifa Israel Institute of Psychology ELTE Eötvös Loránd University Budapest Hungary Institute of Forensic Psychiatry and Sex Research University of Duisburg-Essen Essen Germany Doctoral School of Psychology ELTE Eötvös Loránd University Budapest Hungary Department of Psychology University of Nevada Las Vegas Las Vegas NV United States Centre of Excellence in Responsible Gaming University of Gibraltar Gibraltar Gibraltar Yale University School of Medicine New Haven CT United States Connecticut Council on Problem Gambling Wethersfield CT United States Departmento de Psicología Básica Clínica y Psicobiología University Jaume I of Castellón Spain Institute for Behavioural Addictions Sigmund Freud University Vienna Austria Département de Psychologie Université de Montréal Montréal Canada Institute of Psychology University of Lausanne Lausanne Switzerland Center for Excessive Gambling Addiction Medicine Lausanne University Hospitals (CHUV) Lausanne Switzerland Laboratory of Behavioral Medicine Neuroscience Institute Lithuanian University of Health Sciences Lithuania Virtual Teaching and Cyberpsychology Laboratory School of Psychology National Autonomous University of Mexico Mexico William James Center for Research Departamento de Educação e Psicologia Universidade de Aveiro Aveiro Portugal Department of Personality Assessment and Psychological Treatments University of Valencia Spain Department of Psychology College of Humanity and Social Science Fuzhou University China Section of Sexual Psychopathology Department of Dynamic and Clinical Psychology and Health Studies Sapienza University of Rome Rome Italy Department of Clinical Pharmaceutical and Biological Sciences University of Hertfordshire United Kingdom Department of Psychology and Cognitive Science University of Trento Italy Department of Psychology and Neuroscience Auckland University of Technology Auckland New Z

Objectives: This study aimed to examine the psychometric properties of the P4 suicide screener in a multinational sample. The primary goal was to evaluate the reliability and validity of the scale and investigate its convergent validity by analyzing its correlation with depression, anxiety, and substance use. Study design: The study design is a cross-sectional self-report study conducted across 42 countries. Methods: A cross-sectional, self-report study was conducted in 42 countries, with a total of 82,243 participants included in the final data set. Results: The study provides an overview of suicide ideation rates across 42 countries and confirms the structural validity of the P4 screener. The findings indicated that sexual and gender minority individuals exhibited higher rates of suicidal ideation. The P4 screener showed adequate reliability, convergence, and discriminant validity, and a cutoff score of 1 is recommended to identify individuals at risk of suicidal behavior. Conclusions: The study supports the reliability and validity of the P4 suicide screener across 42 diverse countries, highlighting the importance of using a cross-cultural suicide risk assessment to standardize the identification of high-risk individuals and tailoring culturally sensitive suicide prevention strategies. © 2024 The Royal Society for Public Health

关键词： Assessment Cross-cultural Suicide Validation

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