title: Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample.
abstract: In small, selected samples, an approach combining resting-state functional connectivity MRI and multivariate pattern analysis has been able to successfully classify patients diagnosed with unipolar depression. Purposes of this investigation were to assess the generalizability of this approach to a large clinically more realistic sample and secondarily to assess the replicability of previously reported methodological feasibility in a more homogeneous subgroup with pronounced depressive symptoms. Two independent subsets were drawn from the depression and control cohorts of the BiDirect study, each with 180 patients with and 180 controls without depression. Functional connectivity either among regions covering the gray matter or selected regions with known alterations in depression was assessed by resting-state fMRI. Support vector machines with and without automated feature selection were used to train classifiers differentiating between individual patients and controls in the entire first subset as well as in the subgroup. Model parameters were explored systematically. The second independent subset was used for validation of successful models. Classification accuracies in the large, heterogeneous sample ranged from 45.0 to 56.1% (chance level 50.0%). In the subgroup with higher depression severity, three out of 90 models performed significantly above chance (60.8-61.7% at independent validation). In conclusion, common classification methods previously successful in small homogenous depression samples do not immediately translate to a more realistic population. Future research to develop diagnostic classification approaches in depression should focus on more specific clinical questions and consider heterogeneity, including symptom severity as an important factor.
authors.affiliation: Department of Clinical Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Münster, Germany. benedikt.sundermann@uni-muenster.de.
authors.forename: Benedikt
authors.identifier: 0000-0003-4390-3464
authors.initials: B
authors.lastname: Sundermann
authors.affiliation: Department of Clinical Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Münster, Germany.
authors.forename: Stephan
authors.initials: S
authors.lastname: Feder
authors.affiliation: Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
authors.forename: Heike
authors.initials: H
authors.lastname: Wersching
authors.affiliation: Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
authors.forename: Anja
authors.initials: A
authors.lastname: Teuber
authors.affiliation: Department of Clinical Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Münster, Germany.
authors.forename: Wolfram
authors.initials: W
authors.lastname: Schwindt
authors.affiliation: Department of Clinical Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Münster, Germany.
authors.forename: Harald
authors.initials: H
authors.lastname: Kugel
authors.affiliation: Department of Clinical Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Münster, Germany.
authors.forename: Walter
authors.initials: W
authors.lastname: Heindel
authors.affiliation: Department of Psychiatry, University Hospital Münster, Münster, Germany.
authors.forename: Volker
authors.initials: V
authors.lastname: Arolt
authors.affiliation: Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
authors.forename: Klaus
authors.initials: K
authors.lastname: Berger
authors.affiliation: Department of Clinical Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Münster, Germany.
authors.forename: Bettina
authors.initials: B
authors.lastname: Pfleiderer
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D001931:Brain Mapping; D015331:Cohort Studies; D063132:Connectome; D003865:Major Depressive Disorder / Q000145:classification* / Q000000981:diagnostic imaging* / Q000503:physiopathology; D005240:Feasibility Studies; D005260:Female; D066128:Gray Matter / Q000000981:diagnostic imaging / Q000503:physiopathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009434:Neural Pathways / Q000000981:diagnostic imaging / Q000503:physiopathology; D010363:Pattern Recognition, Automated; D011569:Psychiatric Status Rating Scales; D012146:Rest; D057566:Self Report; D012720:Severity of Illness Index; D060388:Support Vector Machine
keywords: Classification; Depression; Functional connectivity; MVPA; fMRI