[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128369-en":3,"doc-seo-128369-105":31,"detail-sidebar-cat-0-en-105":97},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128369,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Neuroimaging and machine learning in eating disorders: a systematic review","Eating disorders (EDs), including anorexia nervosa, bulimia nervosa, and binge eating disorder, are complex psychiatric conditions with high morbidity and mortality. Neuroimaging combined with machine learning (ML) is positioned as a promising strategy to refine diagnosis, clarify pathophysiological mechanisms, and forecast treatment response. This systematic review evaluates how ML techniques are applied to neuroimaging data across ED subtypes, using PRISMA-guided study selection and formal quality assessment.","Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity (2025) 30:46  \n[https://doi.org/10.1007/s40519-025-01757-w](https://doi.org/10.1007/s40519-025-01757-w)  \nNeuroimaging and machine learning in eating disorders: a systematic review  \nFrancesco Monaco1,2 · Annarita Vignapiano1,2 · Benedetta Di Gruttola1 · Stefania Landi1 · Ernesta Panarello1 · Raffaele Malvone1 · Stefania Palermo1 · Alessandra Marenna2 · Enrico Collantoni3 · Giovanna Celia4 · Valeria Di Stefano5 · Paolo Meneguzzo3 · Martina D’Angelo5 · Giulio Corrivetti1,2 · Luca Steardo Jr.5  \nReceived: 27 March 2025 / Accepted: 16 May 2025 © The Author(s) 2025  \nAbstract  \nPurpose Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED), are complex psychiatric conditions with high morbidity and mortality. Neuroimaging and machine learning (ML) represent promising approaches to improve diagnosis, understand pathophysiological mechanisms, and predict treatment response. This systematic review aimed to evaluate the application of ML techniques to neuroimaging data in EDs.  \nMethods Following PRISMA guidelines (PROSPERO registration: CRD42024628157), we systematically searched PubMed and APA PsycINFO for studies published between 2014 and 2024. Inclusion criteria encompassed human studies using neuroimaging and ML methods applied to AN, BN, or BED. Data extraction focused on study design, imaging modalities, ML techniques, and performance metrics. Quality was assessed using the GRADE framework and the ROBINS-I tool. Results Out of 185 records screened, 5 studies met the inclusion criteria. Most applied support vector machines (SVMs) or other supervised ML models to structural MRI or diffusion tensor imaging data. Cortical thickness alterations in AN and diffusion-based metrics effectively distinguished ED subtypes. However, all studies were observational, heterogeneous, and at moderate to serious risk of bias. Sample sizes were small, and external validation was lacking.  \nConclusion ML applied to neuroimaging shows potential for improving ED characterization and outcome prediction. Nevertheless, methodological limitations restrict generalizability. Future research should focus on larger, multicenter, and multimodal studies to enhance clinical applicability.  \nLevel of Evidence: Level IV, multiple observational studies with methodological heterogeneity and moderate to serious risk of bias.  \nKeywords Neuroimaging · Machine learning · Eating disorders · Biomarkers · Predictive analytic  \nFrancesco Monaco and Annarita Vignapiano have contributed equally to this work and share first authorship.  \n* Benedetta Di Gruttola [bennydgr2@gmail.com](bennydgr2@gmail.com)  \n* Luca Steardo Jr.  \n[steardo@unicz.it](steardo@unicz.it)  \n1 Department of Mental Health, Azienda Sanitaria Locale Salerno, Salerno, Italy  \n2 European Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy  \n3 University of Padova, Padua, Italy  \n4 University Telematica Pegaso, Naples, Italy  \n5 University “Magna Graecia” of Catanzaro, Catanzaro, Italy  \nIntroduction  \nEating disorders (EDs) are serious psychiatric illnesses associated with high morbidity and mortality and characterized by overlapping clinical features such as distorted body image, obsessive–compulsive behaviors, and impaired insight, which contribute to diagnostic challenges and potentially inappropriate treatment [1] . These shared features suggest the involvement of common underlying mechanisms, including psychological traits such as perfectionism and impulsivity, and neurobiological alterations in fronto-striatal circuits involved in reward processing, inhibitory control, and body image perception.  \nWhile this categorical framework facilitates clinical diagnosis and treatment planning, it may oversimplify the substantial heterogeneity observed within and across EDs.  \nIn clinical practice, individuals often exhibit features that span multiple diagnostic categories or ","cbCaibb4YiobNBSs","https://ap.wps.com/l/cbCaibb4YiobNBSs","pdf",805401,7,1,14,"English","en",105,"# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Diagnostic challenges and shared mechanisms\n## Transdiagnostic dimensions and biotypes\n## Neuroimaging and ML as a data-driven approach\n## Anorexia nervosa and bulimia nervosa overview","[{\"question\":\"What is the main objective of the systematic review?\",\"answer\":\"To evaluate the application of machine learning techniques to neuroimaging data in eating disorders and assess how they may support diagnosis, mechanism understanding, and treatment prediction.\"},{\"question\":\"How were studies selected and evaluated in the review?\",\"answer\":\"Studies from 2014 to 2024 were searched in PubMed and APA PsycINFO following PRISMA, with inclusion of human neuroimaging studies using ML for anorexia nervosa, bulimia nervosa, or binge eating disorder. Quality was assessed using GRADE and ROBINS-I.\"},{\"question\":\"What were the key findings about ML models and neuroimaging features?\",\"answer\":\"Five studies met inclusion criteria, most using supervised ML (e.g., support vector machines) on structural MRI or diffusion tensor imaging, with cortical thickness or diffusion-based metrics helping distinguish ED subtypes.\"},{\"question\":\"What limitations affect clinical generalizability according to the review?\",\"answer\":\"The included studies were observational, heterogeneous, at moderate to serious risk of bias, with small sample sizes and limited or absent external validation.\"}]","Neuroimaging and machine learning in eating disorders: a systematic review | PDF",1785947129,35,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":92,"head_meta":94,"extra_data":96,"updated_unix":29},"neuroimaging-and-machine-learning-in-eating-disorders-a-systematic-review","",{"@graph":37,"@context":91},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/neuroimaging-and-machine-learning-in-eating-disorders-a-systematic-review/128369/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-30","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83,87],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main objective of the systematic review?","Question",{"text":77,"@type":78},"To evaluate the application of machine learning techniques to neuroimaging data in eating disorders and assess how they may support diagnosis, mechanism understanding, and treatment prediction.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How were studies selected and evaluated in the review?",{"text":82,"@type":78},"Studies from 2014 to 2024 were searched in PubMed and APA PsycINFO following PRISMA, with inclusion of human neuroimaging studies using ML for anorexia nervosa, bulimia nervosa, or binge eating disorder. 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