title: Structural MRI-Based Predictions in Patients with Treatment-Refractory Depression (TRD).
abstract: The application of machine learning techniques to psychiatric neuroimaging offers the possibility to identify robust, reliable and objective disease biomarkers both within and between contemporary syndromal diagnoses that could guide routine clinical practice. The use of quantitative methods to identify psychiatric biomarkers is consequently important, particularly with a view to making predictions relevant to individual patients, rather than at a group-level. Here, we describe predictions of treatment-refractory depression (TRD) diagnosis using structural T1-weighted brain scans obtained from twenty adult participants with TRD and 21 never depressed controls. We report 85% accuracy of individual subject diagnostic prediction. Using an automated feature selection method, the major brain regions supporting this significant classification were in the caudate, insula, habenula and periventricular grey matter. It was not, however, possible to predict the degree of 'treatment resistance' in individual patients, at least as quantified by the Massachusetts General Hospital (MGH-S) clinical staging method; but the insula was again identified as a region of interest. Structural brain imaging data alone can be used to predict diagnostic status, but not MGH-S staging, with a high degree of accuracy in patients with TRD.
authors.affiliation: Division of Neuroscience, Medical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, United Kingdom.
authors.forename: Blair A
authors.initials: BA
authors.lastname: Johnston
authors.affiliation: Division of Neuroscience, Medical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, United Kingdom; Advanced Interventions Service, Area 7, Level 6, South Block, Ninewells Hospital and Medical School, NHS Tayside, Dundee, United Kingdom.
authors.forename: J Douglas
authors.initials: JD
authors.lastname: Steele
authors.affiliation: Division of Neuroscience, Medical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, United Kingdom.
authors.forename: Serenella
authors.initials: S
authors.lastname: Tolomeo
authors.affiliation: Advanced Interventions Service, Area 7, Level 6, South Block, Ninewells Hospital and Medical School, NHS Tayside, Dundee, United Kingdom.
authors.forename: David
authors.initials: D
authors.lastname: Christmas
authors.affiliation: Division of Neuroscience, Medical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, United Kingdom; Advanced Interventions Service, Area 7, Level 6, South Block, Ninewells Hospital and Medical School, NHS Tayside, Dundee, United Kingdom.
authors.forename: Keith
authors.initials: K
authors.lastname: Matthews
mesh_terms: D016022:Case-Control Studies; D061218:Depressive Disorder, Treatment-Resistant / Q000175:diagnosis* / Q000473:pathology*; D005260:Female; D066128:Gray Matter / Q000473:pathology; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D060388:Support Vector Machine