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Biomarkers might support diagnostic decisions. This study used machine learning on multimodal biobehavioral data from a sample of GAD<\/jats:styled-content>, MD<\/jats:styled-content> and healthy subjects to differentiate subjects with a disorder from healthy subjects (case\u2010classification) and to differentiate GAD<\/jats:styled-content> from MD<\/jats:styled-content> (disorder\u2010classification).<\/jats:p><\/jats:sec>Methods<\/jats:title>Subjects with GAD<\/jats:styled-content> (n<\/jats:italic>\u00a0=\u00a019), MD<\/jats:styled-content> without GAD<\/jats:styled-content> (n<\/jats:italic>\u00a0=\u00a014), and healthy comparison subjects (n<\/jats:italic>\u00a0=\u00a024) were included. The sample was matched regarding age, sex, handedness and education and free of psychopharmacological medication. Binary support vector machines were used within a nested leave\u2010one\u2010out cross\u2010validation framework. Clinical questionnaires, cortisol release, gray matter (GM<\/jats:styled-content>), and white matter (WM<\/jats:styled-content>) volumes were used as input data separately and in combination.<\/jats:p><\/jats:sec>Results<\/jats:title>Questionnaire data were well\u2010suited for case\u2010classification but not disorder\u2010classification (accuracies: 96.40%, p<\/jats:italic>\u00a0<\u00a0.001; 56.58%, p<\/jats:italic>\u00a0>\u00a0.22). The opposite pattern was found for imaging data (case\u2010classification GM<\/jats:styled-content>\/WM<\/jats:styled-content>: 58.71%, p<\/jats:italic>\u00a0=\u00a0.09\/43.18%, p<\/jats:italic>\u00a0>\u00a0.66; disorder\u2010classification GM<\/jats:styled-content>\/WM<\/jats:styled-content>: 68.05%, p<\/jats:italic>\u00a0=\u00a0.034\/58.27%, p<\/jats:italic>\u00a0>\u00a0.15) and for cortisol data (38.02%, p<\/jats:italic>\u00a0=\u00a0.84; 74.60%, p<\/jats:italic>\u00a0=\u00a0.009). All data combined achieved 90.10% accuracy (p<\/jats:italic>\u00a0<\u00a0.001) for case\u2010classification and 67.46% accuracy (p<\/jats:italic>\u00a0=\u00a0.0268) for disorder\u2010classification.<\/jats:p><\/jats:sec>Conclusions<\/jats:title>In line with previous evidence, classification of GAD<\/jats:styled-content> was difficult using clinical questionnaire data alone. Particularly cortisol and GM<\/jats:styled-content> volume data were able to provide incremental value for the classification of GAD<\/jats:styled-content>. Findings suggest that neurobiological biomarkers are a useful target for further research to delineate their potential contribution to diagnostic processes.<\/jats:p><\/jats:sec>","DOI":"10.1002\/brb3.633","type":"journal-article","created":{"date-parts":[[2017,2,12]],"date-time":"2017-02-12T05:12:44Z","timestamp":1486876364000},"source":"Crossref","is-referenced-by-count":90,"title":["Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI<\/scp> data: A multimodal machine learning study"],"prefix":"10.1002","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7986-4113","authenticated-orcid":false,"given":"Kevin","family":"Hilbert","sequence":"first","affiliation":[{"name":"Institute of Clinical Psychology and Psychotherapy Technische Universit\u00e4t Dresden Dresden Germany"},{"name":"Behavioral Epidemiology Technische Universit\u00e4t Dresden Dresden Germany"},{"name":"Department of Psychology Neuroimaging Center Technische Universit\u00e4t Dresden Dresden Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulrike","family":"Lueken","sequence":"additional","affiliation":[{"name":"Institute of Clinical Psychology and Psychotherapy Technische Universit\u00e4t Dresden Dresden Germany"},{"name":"Department of Psychology Neuroimaging Center Technische Universit\u00e4t Dresden Dresden Germany"},{"name":"Department of Psychiatry, Psychosomatics, and Psychotherapy University Hospital Wuerzburg Wuerzburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Muehlhan","sequence":"additional","affiliation":[{"name":"Institute of Clinical Psychology and Psychotherapy Technische Universit\u00e4t Dresden Dresden Germany"},{"name":"Department of Psychology Neuroimaging Center Technische Universit\u00e4t Dresden Dresden Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katja","family":"Beesdo\u2010Baum","sequence":"additional","affiliation":[{"name":"Institute of Clinical Psychology and Psychotherapy Technische Universit\u00e4t Dresden Dresden Germany"},{"name":"Behavioral Epidemiology Technische Universit\u00e4t Dresden Dresden Germany"},{"name":"Department of Psychology Neuroimaging Center Technische Universit\u00e4t Dresden Dresden Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2017,2,12]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-1331.2005.01196.x"},{"key":"e_1_2_7_3_1","volume-title":"Diagnostic and statistical manual of mental disorders, text revision (DSM\u2010IV\u2010TR)","author":"APA","year":"2000"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2007.07.007"},{"key":"e_1_2_7_5_1","volume-title":"Manual for the beck depression inventory","author":"Beck A. 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