title: Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study.
abstract: Generalized anxiety disorder (GAD) is difficult to recognize and hard to separate from major depression (MD) in clinical settings. Biomarkers might support diagnostic decisions. This study used machine learning on multimodal biobehavioral data from a sample of GAD, MD and healthy subjects to differentiate subjects with a disorder from healthy subjects (case-classification) and to differentiate GAD from MD (disorder-classification).
  
  
  
  Subjects with GAD (n= 19), MD without GAD (n= 14), and healthy comparison subjects (n= 24) 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-one-out cross-validation framework. Clinical questionnaires, cortisol release, gray matter (GM), and white matter (WM) volumes were used as input data separately and in combination.
  
  
  
  Questionnaire data were well-suited for case-classification but not disorder-classification (accuracies: 96.40%,p< .001; 56.58%,p> .22). The opposite pattern was found for imaging data (case-classification GM/WM: 58.71%,p= .09/43.18%,p> .66; disorder-classification GM/WM: 68.05%,p= .034/58.27%,p> .15) and for cortisol data (38.02%,p= .84; 74.60%,p= .009). All data combined achieved 90.10% accuracy (p< .001) for case-classification and 67.46% accuracy (p= .0268) for disorder-classification.
  
  
  
  In line with previous evidence, classification of GAD was difficult using clinical questionnaire data alone. Particularly cortisol and GM volume data were able to provide incremental value for the classification of GAD. Findings suggest that neurobiological biomarkers are a useful target for further research to delineate their potential contribution to diagnostic processes.
authors.affiliation: Institute of Clinical Psychology and Psychotherapy Technische Universität Dresden Dresden Germany; Behavioral Epidemiology Technische Universität Dresden Dresden Germany; Department of Psychology Neuroimaging CenterTechnische Universität Dresden Dresden Germany.
authors.forename: Kevin
authors.identifier: 0000-0002-7986-4113
authors.initials: K
authors.lastname: Hilbert
authors.affiliation: Institute of Clinical Psychology and Psychotherapy Technische Universität Dresden Dresden Germany; Department of Psychology Neuroimaging Center Technische Universität Dresden Dresden Germany; Department of Psychiatry, Psychosomatics, and Psychotherapy University Hospital Wuerzburg Wuerzburg Germany.
authors.forename: Ulrike
authors.initials: U
authors.lastname: Lueken
authors.affiliation: Institute of Clinical Psychology and Psychotherapy Technische Universität Dresden Dresden Germany; Department of Psychology Neuroimaging Center Technische Universität Dresden Dresden Germany.
authors.forename: Markus
authors.initials: M
authors.lastname: Muehlhan
authors.affiliation: Institute of Clinical Psychology and Psychotherapy Technische Universität Dresden Dresden Germany; Behavioral Epidemiology Technische Universität Dresden Dresden Germany; Department of Psychology Neuroimaging CenterTechnische Universität Dresden Dresden Germany.
authors.forename: Katja
authors.initials: K
authors.lastname: Beesdo-Baum
mesh_terms: D000328:Adult; D001008:Anxiety Disorders / Q000145:classification / Q000378:metabolism / Q000473:pathology / Q000503:physiopathology; D003865:Major Depressive Disorder / Q000145:classification / Q000378:metabolism / Q000473:pathology / Q000503:physiopathology; D005260:Female; D066128:Gray Matter / Q000000981:diagnostic imaging / Q000473:pathology*; D006801:Humans; D006854:Hydrocortisone / Q000378:metabolism*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D060388:Support Vector Machine; D066127:White Matter / Q000000981:diagnostic imaging / Q000473:pathology*; D055815:Young Adult
chemical_list: D006854:Hydrocortisone
keywords: classification; generalized anxiety disorder; machine learning; major depression; structural MRI; support vector machine