[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125008-en":3,"doc-seo-125008-105":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},125008,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Neuroimaging biomarkers for psychiatry - Predicting diagnosis and treatment outcome using machine learning","Neuroimaging Biomarkers for Psychiatry explores how machine learning can use brain imaging features to support psychiatric diagnosis and to forecast treatment response. The thesis links structural and functional MRI findings with data-driven classification approaches across multiple cohorts, including obsessive-compulsive disorder, anxiety disorders in youth, and treatment outcome after electroconvulsive therapy in depression. By combining multimodal imaging evidence with rigorous model development and validation, the work aims to improve clinical decision-making and enable more personalized interventions.","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:13 Jan 2025  \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","cbCaigemKhjcqJIW","https://ap.wps.com/l/cbCaigemKhjcqJIW","pdf",31560858,1,253,"English","en",105,"# General introduction\n## Introduction\n# Studies\n## Diagnostic neuroimaging markers of obsessive-compulsive disorder: initial evidence from structural and functional MRI studies\n## Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters\n## The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium\n## Brain-based classification of youth with anxiety disorders: an ENIGMA-ANXIETY transdiagnostic examination using machine learning\n## Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis\n# General discussion\n## Summary of main findings\n## General discussion\n# References\n# Appendix","[{\"question\":\"这项论文研究的核心问题是什么？\",\"answer\":\"论文聚焦于利用神经影像生物标志物与机器学习，来预测精神科的诊断结果以及治疗结局。\"},{\"question\":\"论文包含哪些主要研究方向或疾病场景？\",\"answer\":\"主要涵盖强迫症（含结构与功能MRI证据）、ENIGMA-OCD用药相关的结构标志物、强迫症静息态连接组与分类、青少年焦虑障碍的跨诊断检查，以及抑郁症电休克治疗结局的多模态生物标志物开发与验证。\"},{\"question\":\"机器学习在这项研究中如何发挥作用？\",\"answer\":\"通过对结构/功能影像特征进行数据驱动分析与模型训练，使用分类或预测框架来对诊断或治疗结局进行预测，并在多中心或大型队列层面开展验证。\"}]","Neuroimaging biomarkers for psychiatry - Predicting diagnosis and treatment outcome using machine learning | PDF",1785896088,638,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"neuroimaging-biomarkers-for-psychiatry-predicting-diagnosis-and-treatment-outcome-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/neuroimaging-biomarkers-for-psychiatry-predicting-diagnosis-and-treatment-outcome-using-machine-learning/125008/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"这项论文研究的核心问题是什么？","Question",{"text":75,"@type":76},"论文聚焦于利用神经影像生物标志物与机器学习，来预测精神科的诊断结果以及治疗结局。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"论文包含哪些主要研究方向或疾病场景？",{"text":80,"@type":76},"主要涵盖强迫症（含结构与功能MRI证据）、ENIGMA-OCD用药相关的结构标志物、强迫症静息态连接组与分类、青少年焦虑障碍的跨诊断检查，以及抑郁症电休克治疗结局的多模态生物标志物开发与验证。",{"name":82,"@type":73,"acceptedAnswer":83},"机器学习在这项研究中如何发挥作用？",{"text":84,"@type":76},"通过对结构/功能影像特征进行数据驱动分析与模型训练，使用分类或预测框架来对诊断或治疗结局进行预测，并在多中心或大型队列层面开展验证。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]