title: Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder.
abstract: Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n =25) and healthy controls (n =25), SVM learning accurately (74%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information.
authors.affiliation: Department of Psychology, University of Texas at Austin, Austin, TX, USA. Electronic address: schnyer@utexas.edu.
authors.forename: David M
authors.initials: DM
authors.lastname: Schnyer
authors.affiliation: Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA.
authors.forename: Peter C
authors.initials: PC
authors.lastname: Clasen
authors.affiliation: Department of Psychology, University of California, San Diego, San Diego, CA, USA.
authors.forename: Christopher
authors.initials: C
authors.lastname: Gonzalez
authors.affiliation: Department of Psychology, University of Texas at Austin, Austin, TX, USA.
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Beevers
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging; D001931:Brain Mapping / Q000379:methods; D003865:Major Depressive Disorder / Q000000981:diagnostic imaging* / Q000523:psychology; D056324:Diffusion Tensor Imaging / Q000379:methods / Q000592:standards*; D005260:Female; D006801:Humans; D000069550:Machine Learning / Q000592:standards*; D060388:Support Vector Machine / Q000592:standards; D066127:White Matter / Q000000981:diagnostic imaging; D055815:Young Adult