[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125565-en":3,"doc-seo-125565-105":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},125565,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Anxiety onset in adolescents - a machine-learning prediction","Adolescent anxiety disorders show significant disease burden, yet longitudinal studies have not established whether childhood-to-adolescence MRI and psychometric measures can predict who will develop clinical anxiety later. This study uses a voting ensemble combining Random Forest, Support Vector Machine, and Logistic Regression to evaluate gray matter volumes of interest and questionnaire features. Using Shapley values for interpretability, pooled anxiety disorders are predicted mainly from psychometric data with moderate performance, while MRI regional volumes add value for generalized anxiety disorder. Results support individualized prospective prediction several years after baseline.","[https://helda.helsinki.fi](https://helda.helsinki.fi)  \n\n| Anxiety onset in adolescents : a machine-learning prediction\u003Cbr>IMAGEN Consortium\u003Cbr>2023-02 |\n| --- |\n| IMAGEN Consortium , Chavanne , A V , Paillère Martinot , M L , Penttilä , J & Frouin , V\u003Cbr>2023 , ' Anxiety onset in adolescents : a machine-learning prediction ' , Molecular Psychiatry\u003Cbr>þÿ , v o l . 2 8 , p p . 6 3 9 6 4 6 . h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 3 8 0-0 2 2-0 1 8 4 0-z |\n| [http://hdl.handle.net/10138/355586](http://hdl.handle.net/10138/355586)\u003Cbr>[https://doi.org/10.1038/s41380-022-01840-z](https://doi.org/10.1038/s41380-022-01840-z) |\n| cc_by\u003Cbr>publishedVersion |\n\nDownloaded from Helda, University of Helsinki institutional repository. This is an electronic reprint of the original article.  \nThis reprint may differ from the original in pagination and typographic detail. Please cite the original version.  \nMolecular [Psychiatry](Psychiatry www.nature.com/mp)[ www.nature.com/mp](Psychiatry www.nature.com/mp)  \nARTICLE OPEN   \nAnxiety onset in adolescents: a machine-learning prediction  \nAlice V. Chavanne 1,2, Marie Laure Paillère Martinot 1,3, Jani Penttilä4, Yvonne Grimmer5, Patricia Conrod6, Argyris Stringaris7, Betteke van Noort 8, Corinna Isensee9, Andreas Becker9, Tobias Banaschewski 5, Arun L. W. Bokde 10, Sylvane Desrivières 11, Herta Flor 12,13, Antoine Grigis 14, Hugh Garavan15, Penny Gowland 16, Andreas Heinz 17, Rüdiger Brühl 18, Frauke Nees 5,12,19, Dimitri Papadopoulos Orfanos 14, Tomáš Paus 20, Luise Poustka9, Sarah Hohmann5, Sabina Millenet5, Juliane H. Fröhner 21, Michael N. Smolka 21, Henrik Walter 17, Robert Whelan 22, Gunter Schumann 23, Jean-Luc Martinot 1,45 ✉, Eric Artiges 1,24,45 for the IMAGEN consortium*  \n© The Author(s) 2022  \n|  |  |  |\n| --- | --- | --- |\n|  | Recent longitudinal studies in youth have reported MRI correlates of prospective anxiety symptoms during adolescence, a vulnerable period for the onset of anxiety disorders. However, their predictive value has not been established. Individual prediction through machine-learning algorithms might help bridge the gap to clinical relevance. A voting classifier with Random Forest, Support Vector Machine and Logistic Regression algorithms was used to evaluate the predictive pertinence of gray matter volumes of interest and psychometric scores in the detection of prospective clinical anxiety. Participants with clinical anxiety at age 18–23 (N = 156) were investigated at age 14 along with healthy controls (N = 424) . Shapley values were extracted for in-depth interpretation of feature importance. Prospective prediction of pooled anxiety disorders relied mostly on psychometric features and achieved moderate performance (area under the receiver operating curve = 0.68), while generalized anxiety disorder (GAD) prediction achieved similar performance. MRI regional volumes did not improve the prediction performance of prospective pooled anxiety disorders with respect to psychometric features alone, but they improved the prediction performance of GAD, with thecaudate and pallidum volumes being among the most contributing features. To conclude, in non-anxious 14 year old adolescents, future clinical anxiety onset 4–8 years later could be individually predicted. Psychometric features such as neuroticism, hopelessness and emotional symptoms were the main contributors to pooled anxiety disorders prediction. Neuroanatomical data, such as caudate and pallidum volume, proved valuable for GAD and should be included in prospective clinical anxiety prediction |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n| in adolescents. |  |  |\n|  | Molecular Psychiatry (2023) 28:639–646; [https://doi.org/10.1038/s41380-022-01840-z](https://doi.org/10.1038/s41380-022-01840-z) |  |\n|  |  |  |\n\nINTRODUCTION  \nAnxiety disorders have been reported to have a high impact on the global burd","cbCaivvXqV7HdTvk","https://ap.wps.com/l/cbCaivvXqV7HdTvk","pdf",2036589,1,9,"English","en",105,"# Introduction\n## Study rationale and knowledge gap\n## Predictive modeling approach and interpretability","[{\"question\":\"What gap does this study address about adolescent anxiety?\",\"answer\":\"Longitudinal MRI and psychometric findings have correlations with anxiety symptoms, but their predictive value for later clinical anxiety has not been established.\"},{\"question\":\"Which data types were used to predict future clinical anxiety?\",\"answer\":\"The model used MRI gray matter volumes of interest and psychometric scores collected when participants were around age 14.\"},{\"question\":\"What features contributed most to predicting pooled anxiety disorders versus generalized anxiety disorder?\",\"answer\":\"Pooled anxiety disorders relied mainly on psychometric features such as neuroticism, hopelessness, and emotional symptoms, while generalized anxiety disorder prediction benefited from MRI regional volumes, especially caudate and pallidum.\"}]","Anxiety onset in adolescents - a machine-learning prediction | PDF",1785899887,23,{"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},"anxiety-onset-in-adolescents-a-machine-learning-prediction","",{"@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/anxiety-onset-in-adolescents-a-machine-learning-prediction/125565/",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},"What gap does this study address about adolescent anxiety?","Question",{"text":75,"@type":76},"Longitudinal MRI and psychometric findings have correlations with anxiety symptoms, but their predictive value for later clinical anxiety has not been established.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data types were used to predict future clinical anxiety?",{"text":80,"@type":76},"The model used MRI gray matter volumes of interest and psychometric scores collected when participants were around age 14.",{"name":82,"@type":73,"acceptedAnswer":83},"What features contributed most to predicting pooled anxiety disorders versus generalized anxiety disorder?",{"text":84,"@type":76},"Pooled anxiety disorders relied mainly on psychometric features such as neuroticism, hopelessness, and emotional symptoms, while generalized anxiety disorder prediction benefited from MRI regional volumes, especially caudate and pallidum.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]