[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125449-en":3,"doc-seo-125449-105":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},125449,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Neuroimaging biomarkers for psychiatry - Predicting diagnosis and treatment outcome using machine learning","This thesis develops and evaluates neuroimaging biomarkers for psychiatry with a focus on predicting diagnosis and treatment outcome using machine learning approaches. It investigates diagnostic neuroimaging markers in obsessive-compulsive disorder through structural and functional MRI evidence, and tests structural biomarkers within the ENIGMA-OCD consortium while examining medication effects. Additional work performs resting-state connectome mega-analyses and machine-learning classification for ENIGMA-OCD, and applies a transdiagnostic ENIGMA-ANXIETY framework to youth anxiety disorders.","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:05 Jan 2026  \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","cbCaisAvrK3qUXwE","https://ap.wps.com/l/cbCaisAvrK3qUXwE","pdf",31540653,1,253,"English","en",105,"# Part I General introduction\n## 1 Introduction\n## The burden of mental illness: a call for action\n## Diagnostic classification in mental health\n# II Studies\n## 2 Diagnostic neuroimaging markers of obsessive-compulsive disorder: initial evidence from structural and functional MRI studies\n## 3 Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters\n## 4 The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium\n## 5 Brain-based classification of youth with anxiety disorders: an ENIGMA-ANXIETY transdiagnostic examination using machine learning\n## 6 Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis\n# III General discussion\n## 7 Summary of main findings\n## 8 General discussion\n# IV References\n# V Appendix\n## Nederlandse samenvatting\n## Portfolio\n## Curriculum vitae\n## Author contributions\n## Acknowledgments","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To predict psychiatric diagnosis and treatment outcomes using neuroimaging biomarkers and machine learning.\"},{\"question\":\"Which psychiatric conditions are examined in the studies?\",\"answer\":\"Obsessive-compulsive disorder and youth anxiety disorders are studied, alongside depression treatment outcome for electroconvulsive therapy.\"},{\"question\":\"How do the studies use neuroimaging data?\",\"answer\":\"They combine structural and functional MRI evidence, resting-state connectome mega-analyses, and multimodal approaches across multicenter and consortium datasets.\"}]","Neuroimaging biomarkers for psychiatry - 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