[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125014-en":3,"doc-seo-125014-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125014,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Neuroimaging biomarkers for psychiatry - Predicting diagnosis and treatment outcome using machine learning","This thesis develops and evaluates neuroimaging biomarker approaches for psychiatry, focusing on how brain measures can support diagnosis and predict treatment response. Using structural and functional MRI-derived markers, it examines transdiagnostic risk in youth with anxiety disorders and applies machine-learning classification within major consortia such as ENIGMA-OCD. The work also investigates medication-related effects and estimates outcomes for electroconvulsive therapy in depression using multicenter multimodal analyses.","UvA-DARE (Digital Academic Repository)  \nNeuroimaging biomarkers for psychiatry  \nPredicting diagnosis and treatment outcome using machine learning Bruin, W. B.  \nPublication date  \n2024  \nDocument Version  \nFinal published version  \nLink to publication  \nCitation for published version (APA):  \nBruin, W. B. (2024) . Neuroimaging biomarkers for psychiatry: Predicting diagnosis and treatment outcome using machine learning. [Thesis, fully internal, Universiteit van Amsterdam] .  \nGeneral rights  \nIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) .  \nDisclaimer/Complaints regulations  \nIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl))  \nDownload date:18 Oct 2024  \nNeuroimaging Biomarkers for Psychiatry  \nPredicting Diagnosis and Treatment Outcome using Machine Learning  \nThe research in this thesis was supported by research grants from the Netherlands Organization for Scientific Research (NWO/ZonMW Vidi 016.156.318) .  \nLATEX template: Isidoor Bergfeld with additions from Paul Zhutovsky  \n© 2023, Willem Bruin  \nNeuroimaging biomarkers for psychiatry Predicting diagnosis and treatment outcome using machine learning  \nACADEMISCH PROEFSCHRIFT  \nter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus [prof. dr. ir. P.P.C.C. Verbeek](prof. dr. ir. P.P.C.C. Verbeek)  \nten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel op donderdag 4 april 2024, te 16.00 uur  \ndoor Willem Benjamin Bruingeboren te Amsterdam  \nPromotiecommissie  \nPromotores:  \nCopromotores:  \nOverige leden:  \nprof. dr. G.A. van Wingen prof. dr. D.A.J.P. Denys  \ndr. R.M. Thomas  \ndr. P. Zhutovsky  \nprof. dr. L. Reneman prof. dr. H. Bruining prof. dr. N.J.A. van der Wee prof. dr. D.C. Cath  \ndr. P.P. de Koning  \nAMC-UvA  \nAMC-UvA  \nAMC-UvA  \nAMC-UvA  \nAMC-UvA  \nAMC-UvA  \nUniversiteit Leiden Rijksuniversiteit Groningen AMC-UvA  \nFaculteit der Geneeskunde  \n\n| Contents |  |\n| --- | --- |\n| I General introduction | 7 |\n| 1 Introduction | 8 |\n| II Studies | 18 |\n| 2 Diagnostic neuroimaging markers of obsessive-compulsive disorder: initial evidence from structural and functional MRI studies | 19 |\n| 3 Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters | 42 |\n| 4 The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium | 65 |\n| 5 Brain-based classification of youth with anxiety disorders: an ENIGMA-ANXIETY transdiagnostic examination using machine learning | 95 |\n| 6 Development and validation of a multimodal neuroimaging |  |\n| biomarker for electroconvulsive therapy outcome in depres- |  |\n| sion: a multicenter machine learning analysis | 131 |\n| III General discussion | 178 |\n\n7 Summary of main findings 179  \n8 General discussion 184  \n\n| IV | References | 197 |\n| --- | --- | --- |\n| V | Appendix | 226 |\n\nNederlandse samenvatting 227  \nPortfolio 233  \nCurriculum vitae 242  \nAuthor contributions 244  \nAcknowledgments 248  \nPART I  \nG","cbCaicZKPkcuf8mh","https://ap.wps.com/l/cbCaicZKPkcuf8mh","pdf",31560989,1,253,"English","en",105,"# General introduction\n## Introduction\n## Diagnostic classification in mental health\n# Studies\n## Diagnostic neuroimaging markers of obsessive-compulsive disorder\n## Structural neuroimaging biomarkers for obsessive-compulsive disorder in ENIGMA-OCD\n## The functional connectome in obsessive-compulsive disorder\n## Brain-based classification of youth with anxiety disorders\n## Development and validation of a multimodal neuroimaging biomarker for ECT outcome\n# General discussion\n## Summary of main findings\n## General discussion\n# References\n# Appendix","[{\"question\":\"What is the main objective of the thesis?\",\"answer\":\"The thesis aims to develop and validate neuroimaging biomarkers that can predict psychiatric diagnosis and treatment outcome using machine learning.\"},{\"question\":\"Which neuroimaging domains are used in the studies?\",\"answer\":\"The work includes structural MRI and functional MRI measures, including connectome-based analyses and multimodal biomarker development.\"},{\"question\":\"How does the thesis assess treatment outcome?\",\"answer\":\"It evaluates a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression using a multicenter machine-learning analysis.\"}]","Neuroimaging biomarkers for psychiatry - 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