title: Random Forest Classification of Depression Status Based On Subcortical Brain Morphometry Following Electroconvulsive Therapy.
abstract: Disorders of the central nervous system are often accompanied by brain abnormalities detectable with MRI. Advances in biomedical imaging and pattern detection algorithms have led to classification methods that may help diagnose and track the progression of a brain disorder and/or predict successful response to treatment. These classification systems often use high-dimensional signals or images, and must handle the computational challenges of high dimensionality as well as complex data types such as shape descriptors. Here, we used shape information from subcortical structures to test a recently developed feature-selection method based on regularized random forests to 1) classify depressed subjects versus controls, and 2) patients before and after treatment with electroconvulsive therapy. We subsequently compared the classification performance of high-dimensional shape features with traditional volumetric measures. Shape-based models outperformed simple volumetric predictors in several cases, highlighting their utility as potential automated alternatives for establishing diagnosis and predicting treatment response.
authors.affiliation: Imaging Genetics Center, University of Southern California.
authors.forename: Benjamin S C
authors.initials: BS
authors.lastname: Wade
authors.affiliation: Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA.
authors.forename: Shantanu H
authors.initials: SH
authors.lastname: Joshi
authors.affiliation: Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA.
authors.forename: Tara
authors.initials: T
authors.lastname: Pirnia
authors.affiliation: Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA.
authors.forename: Amber M
authors.initials: AM
authors.lastname: Leaver
authors.affiliation: Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA ; Department of Psychiatry and Biobehavioral Sciences, UCLA.
authors.forename: Roger P
authors.initials: RP
authors.lastname: Woods
authors.affiliation: Imaging Genetics Center, University of Southern California ; Department of Psychiatry and Biobehavioral Sciences, UCLA.
authors.forename: Paul M
authors.initials: PM
authors.lastname: Thompson
authors.affiliation: Department of Psychiatry and Biobehavioral Sciences, UCLA.
authors.forename: Randall
authors.initials: R
authors.lastname: Espinoza
authors.affiliation: Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA ; Department of Psychiatry and Biobehavioral Sciences, UCLA.
authors.forename: Katherine L
authors.initials: KL
authors.lastname: Narr
keywords: Random forest; classification; electroconvulsive therapy; feature selection; major depressive disorder; regularization; shape analysis