{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T05:07:23Z","timestamp":1783228043686,"version":"3.54.6"},"reference-count":44,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T00:00:00Z","timestamp":1739232000000},"content-version":"vor","delay-in-days":41,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["NIMH R01 MH128286"],"award-info":[{"award-number":["NIMH R01 MH128286"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["NIMH R01 MH132962"],"award-info":[{"award-number":["NIMH R01 MH132962"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DGE-2139839"],"award-info":[{"award-number":["DGE-2139839"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Washington University Research Computing and Informatics Facility"},{"name":"NIH S10","award":["1S10OD025200-01A1"],"award-info":[{"award-number":["1S10OD025200-01A1"]}]},{"name":"NIH S10","award":["1S10OD030477-01"],"award-info":[{"award-number":["1S10OD030477-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The use of machine learning to classify diagnostic cases versus controls defined based on diagnostic ontologies such as the International Classification of Diseases, Tenth Revision (ICD-10) from neuroimaging features is now commonplace across a wide range of diagnostic fields. However, transdiagnostic comparisons of such classifications are lacking. Such transdiagnostic comparisons are important to establish the specificity of classification models, set benchmarks, and assess the value of diagnostic ontologies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We investigated case-control classification accuracy in 17 different ICD-10 diagnostic groups from Chapter V (mental and behavioral disorders) and Chapter VI (diseases of the nervous system) using data from the UK Biobank. Classification models were trained using either neuroimaging (structural or functional brain magnetic resonance imaging feature sets) or sociodemographic features. Random forest classification models were adopted using rigorous shuffle-splits to estimate stability as well as accuracy of case-control classifications. Diagnostic classification accuracies were benchmarked against age classification (oldest vs. youngest) from the same feature sets and against additional classifier types (k-nearest neighbors and linear support vector machine). In contrast to age classification accuracy, which was high for all feature sets, few ICD-10 diagnostic groups were classified significantly above chance (namely, demyelinating diseases based on structural neuroimaging features and depression based on sociodemographic and functional neuroimaging features).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings highlight challenges with the current disease classification system, leading us to recommend caution with the use of ICD-10 diagnostic groups as target labels in brain-based disease prediction studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/gigascience\/giae119","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T03:00:23Z","timestamp":1739242823000},"source":"Crossref","is-referenced-by-count":1,"title":["Opaque ontology: neuroimaging classification of ICD-10 diagnostic groups in the UK Biobank"],"prefix":"10.1093","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8215-2598","authenticated-orcid":false,"given":"Ty","family":"Easley","sequence":"first","affiliation":[{"name":"Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8028-3822","authenticated-orcid":false,"given":"Xiaoke","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6340-6791","authenticated-orcid":false,"given":"Kayla","family":"Hannon","sequence":"additional","affiliation":[{"name":"Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1446-8034","authenticated-orcid":false,"given":"Petra","family":"Lenzini","sequence":"additional","affiliation":[{"name":"Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1385-9178","authenticated-orcid":false,"given":"Janine","family":"Bijsterbosch","sequence":"additional","affiliation":[{"name":"Department of Radiology, Washington University School of Medicine , Saint Louis, Missouri 63110 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,2,11]]},"reference":[{"key":"2025022109491170400_bib1","doi-asserted-by":"publisher","first-page":"3468","DOI":"10.1002\/hbm.25013","article-title":"Towards a brain-based predictome of mental illness","volume":"41","author":"Rashid","year":"2020","journal-title":"Hum Brain Mapp"},{"key":"2025022109491170400_bib2","doi-asserted-by":"publisher","first-page":"e1001779","DOI":"10.1371\/journal.pmed.1001779","article-title":"UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age","volume":"12","author":"Sudlow","year":"2015","journal-title":"PLoS Med"},{"key":"2025022109491170400_bib3","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1038\/nn.4393","article-title":"Multimodal population brain imaging in the UK Biobank prospective epidemiological study","volume":"19","author":"Miller","year":"2016","journal-title":"Nat Neurosci"},{"key":"2025022109491170400_bib4","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1093\/aje\/kwx246","article-title":"Comparison of sociodemographic and health-related characteristics of UK biobank participants with those of the general population","volume":"186","author":"Fry","year":"2017","journal-title":"Am J Epidemiol"},{"key":"2025022109491170400_bib5","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.hlpt.2012.07.003","article-title":"UK Biobank: Current status and what it means for epidemiology","volume":"1","author":"Allen","year":"2012","journal-title":"Health Policy Technol"},{"key":"2025022109491170400_bib6","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.schres.2019.01.018","article-title":"Cross-diagnostic analysis of cognitive control in mental illness: Insights from the CNTRACS consortium","volume":"208","author":"Smucny","year":"2019","journal-title":"Schizophr Res"},{"key":"2025022109491170400_bib7","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1186\/s13195-022-00983-z","article-title":"Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes","volume":"14","author":"Lampe","year":"2022","journal-title":"Alzheimers Res Ther"},{"key":"2025022109491170400_bib8","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.ijmedinf.2006.11.005","article-title":"Reliability of diagnoses coding with ICD-10","volume":"77","author":"Stausberg","year":"2008","journal-title":"Int J Med Informatics"},{"key":"2025022109491170400_bib9","doi-asserted-by":"publisher","first-page":"e1590","DOI":"10.1002\/mpr.1590","article-title":"Reliability and validity of severity dimensions of psychopathology assessed using the Structured Clinical Interview for DSM-5 (SCID)","volume":"27","author":"Shankman","year":"2018","journal-title":"Int J Methods Psychiatr Res"},{"key":"2025022109491170400_bib10","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1176\/appi.ajp.2012.12070999","article-title":"DSM-5 field trials in the United States and Canada, part II: test-retest reliability of selected categorical diagnoses","volume":"170","author":"Regier","year":"2013","journal-title":"Am J Psychiatry"},{"key":"2025022109491170400_bib11","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1111\/j.1526-4637.2008.00527.x","article-title":"Provider judgments of patients in pain: Seeking symptom certainty","volume":"10","author":"Tait","year":"2009","journal-title":"Pain Med"},{"key":"2025022109491170400_bib12","doi-asserted-by":"publisher","first-page":"1124","DOI":"10.1126\/science.185.4157.1124","article-title":"Judgment under uncertainty: Heuristics and biases","volume":"185","author":"Tversky","year":"1974","journal-title":"Science"},{"key":"2025022109491170400_bib13","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1370\/afm.749","article-title":"Influence of patients\u2019 socioeconomic status on clinical management decisions: A qualitative study","volume":"6","author":"Bernheim","year":"2008","journal-title":"Ann Fam Med"},{"key":"2025022109491170400_bib14","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1016\/S0277-9536(99)00338-X","article-title":"The effect of patient race and socio-economic status on physicians\u2019 perceptions of patients","volume":"50","author":"van\u00a0Ryn","year":"2000","journal-title":"Soc Sci Med"},{"key":"2025022109491170400_bib15","doi-asserted-by":"publisher","first-page":"e059837","DOI":"10.1136\/bmjopen-2021-059837","article-title":"Health professional's implicit bias of adult patients with low socioeconomic status (SES) and its effects on clinical decision-making: a scoping review protocol","volume":"12","author":"Job","year":"2022","journal-title":"BMJ Open"},{"key":"2025022109491170400_bib16","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1176\/appi.ps.201800223","article-title":"A naturalistic study of racial disparities in diagnoses at an outpatient Behavioral Health clinic","volume":"70","author":"Gara","year":"2019","journal-title":"Psychiatr Serv"},{"key":"2025022109491170400_bib17","doi-asserted-by":"publisher","first-page":"984","DOI":"10.1086\/704756","article-title":"Upcoding: evidence from Medicare on squishy risk adjustment","volume":"128","author":"Geruso","year":"2020","journal-title":"J Polit Econ"},{"key":"2025022109491170400_bib18","first-page":"1f","article-title":"Upcoding Medicare: Is healthcare fraud and abuse increasing?","volume":"18","author":"Coustasse","year":"2021","journal-title":"Perspect Health Inf Manag"},{"key":"2025022109491170400_bib19","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/ICDMW.2010.108","article-title":"Unsupervised DRG upcoding detection in healthcare databases","volume-title":"2010 IEEE International Conference on Data Mining Workshops","author":"Luo","year":"2010"},{"key":"2025022109491170400_bib20","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1002\/hec.3851","article-title":"Prospective payment systems and discretionary coding-evidence from English mental health providers","volume":"28","author":"Moscelli","year":"2019","journal-title":"Health Econ"},{"key":"2025022109491170400_bib21","doi-asserted-by":"publisher","DOI":"10.1101\/2022.07.22.501193","article-title":"Suboptimal phenotypic reliability impedes reproducible human neuroscience","author":"Nikolaidis","year":"2022","journal-title":"Biorxiv"},{"key":"2025022109491170400_bib22","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-54022-6","article-title":"How measurement noise limits the accuracy of brain-behaviour predictions","volume":"15","author":"Gell","year":"2024","journal-title":"Nature Comms"},{"key":"2025022109491170400_bib23","doi-asserted-by":"publisher","first-page":"giab071","DOI":"10.1093\/gigascience\/giab071","article-title":"Population modeling with machine learning can enhance measures of mental health","volume":"10","author":"Dadi","year":"2021","journal-title":"Gigascience"},{"key":"2025022109491170400_bib24","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1001\/jamapsychiatry.2022.1780","article-title":"Quantifying deviations of brain structure and function in major depressive disorder across neuroimaging modalities","volume":"79","author":"Winter","year":"2022","journal-title":"JAMA Psychiatry"},{"key":"2025022109491170400_bib25","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/S2215-0366(15)00579-9","article-title":"Precision psychiatry: A neural circuit taxonomy for depression and anxiety","volume":"3","author":"Williams","year":"2016","journal-title":"Lancet Psychiatry"},{"key":"2025022109491170400_bib26","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1126\/science.1222940","article-title":"Are we getting closer to valid translational models for major depression?","volume":"338","author":"Berton","year":"2012","journal-title":"Science"},{"key":"2025022109491170400_bib27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jad.2008.11.021","article-title":"Structural brain abnormalities in major depressive disorder: A selective review of recent MRI studies","volume":"117","author":"Lorenzetti","year":"2009","journal-title":"J Affect Disord"},{"key":"2025022109491170400_bib28","doi-asserted-by":"publisher","first-page":"e8170","DOI":"10.7717\/peerj.8170","article-title":"Brain structural and functional changes in patients with major depressive disorder: A literature review","volume":"7","author":"Dai","year":"2019","journal-title":"PeerJ"},{"key":"2025022109491170400_bib29","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1007\/s11682-021-00494-9","article-title":"Structural and functional neuroimaging of late-life depression: A coordinate-based meta-analysis","volume":"16","author":"Saberi","year":"2022","journal-title":"Brain Imaging Behav"},{"key":"2025022109491170400_bib30","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41586-022-04492-9","article-title":"Reproducible brain-wide association studies require thousands of individuals","volume":"603","author":"Marek","year":"2022","journal-title":"Nature"},{"key":"2025022109491170400_bib31","doi-asserted-by":"publisher","first-page":"e0275816","DOI":"10.1371\/journal.pone.0275816","article-title":"Mapping of UK Biobank clinical codes: Challenges and possible solutions","volume":"17","author":"Stroganov","year":"2022","journal-title":"PLoS One"},{"key":"2025022109491170400_bib32","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.neuroimage.2017.10.034","article-title":"Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank","volume":"166","author":"Alfaro-Almagro","year":"2018","journal-title":"Neuroimage"},{"key":"2025022109491170400_bib33","first-page":"147","article-title":"Random forest algorithm in big data environment","volume":"18","author":"Liu","year":"2014","journal-title":"Comput Model New Technol"},{"key":"2025022109491170400_bib34","doi-asserted-by":"publisher","first-page":"100163","DOI":"10.1016\/j.ynirp.2023.100163","article-title":"Population modeling with machine learning can enhance measures of mental health\u2014open-data replication","volume":"3","author":"Easley","year":"2023","journal-title":"Neuroimage Rep"},{"key":"2025022109491170400_bib35","doi-asserted-by":"publisher","first-page":"3095","DOI":"10.1093\/cercor\/bhx179","article-title":"Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI","volume":"28","author":"Schaefer","year":"2018","journal-title":"Cereb Cortex"},{"key":"2025022109491170400_bib36","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1109\/TMI.2003.822821","article-title":"Probabilistic independent component analysis for functional magnetic resonance imaging","volume":"23","author":"Beckmann","year":"2004","journal-title":"IEEE Trans Med Imaging"},{"key":"2025022109491170400_bib37","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3389\/fnins.2017.00115","article-title":"Using dual regression to investigate network shape and amplitude in functional connectivity analyses","volume":"11","author":"Nickerson","year":"2017","journal-title":"Front Neurosci"},{"key":"2025022109491170400_bib38","doi-asserted-by":"publisher","first-page":"117226","DOI":"10.1016\/j.neuroimage.2020.117226","article-title":"Modelling subject variability in the spatial and temporal characteristics of functional modes","volume":"222","author":"Harrison","year":"2020","journal-title":"Neuroimage"},{"key":"2025022109491170400_bib39","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.neuroimage.2015.01.013","article-title":"Large-scale probabilistic functional modes from resting state fMRI","volume":"109","author":"Harrison","year":"2015","journal-title":"Neuroimage"},{"key":"2025022109491170400_bib40","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.neuroimage.2017.07.014","article-title":"Investigations into within- and between-subject resting-state amplitude variations","volume":"159","author":"Bijsterbosch","year":"2017","journal-title":"Neuroimage"},{"key":"2025022109491170400_bib41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41593-020-00726-z","article-title":"Challenges and future directions for representations of functional brain organization","volume":"23","author":"Bijsterbosch","year":"2020","journal-title":"Nat Neurosci"},{"key":"2025022109491170400_bib42","doi-asserted-by":"publisher","first-page":"118533","DOI":"10.1016\/j.neuroimage.2021.118533","article-title":"Recent developments in representations of the connectome","volume":"243","author":"Bijsterbosch","year":"2021","journal-title":"Neuroimage"},{"key":"2025022109491170400_bib43","volume-title":"Opaque ontology: Neuroimaging classification of ICD-10 diagnostic groups in the UK Biobank (version 1) [Computer software]","author":"Easley","year":"2024"},{"key":"2025022109491170400_bib44","doi-asserted-by":"crossref","DOI":"10.1101\/2024.04.15.589555","article-title":"Opaque ontology: Neuroimaging classification of ICD-10 diagnostic groups in the UK Biobank","volume-title":"DOME Registry","author":"Easley","year":"2024"}],"container-title":["GigaScience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/gigascience\/article-pdf\/doi\/10.1093\/gigascience\/giae119\/61840063\/giae119.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/gigascience\/article-pdf\/doi\/10.1093\/gigascience\/giae119\/61840063\/giae119.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T04:59:37Z","timestamp":1740113977000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/gigascience\/article\/doi\/10.1093\/gigascience\/giae119\/8008389"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1093\/gigascience\/giae119","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.04.15.589555","asserted-by":"object"}]},"ISSN":["2047-217X"],"issn-type":[{"value":"2047-217X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025]]},"published":{"date-parts":[[2025]]},"article-number":"giae119"}}