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 re

SUBMITTER: Johnston BA 

PROVIDER: S-EPMC4506147 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature