Random forest classification of depression status based on subcortical brain morphometry following electroconvulsive therapy

• Published in: Proceedings (International Symposium on Biomedical Imaging) Vol. 2015; pp. 92 - 96
• Main Authors: Wade, Benjamin S. C., Joshi, Shantanu H., Pirnia, Tara, Leaver, Amber M., Woods, Roger P., Thompson, Paul M., Espinoza, Randall, Narr, Katherine L.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.04.2015
• Subjects: classification; Diseases; electroconvulsive therapy; feature selection; major depressive disorder; Radio frequency; Random forest; regularization; Shape; shape analysis; Solid modeling; Surface morphology; Volume measurement; major depressive disorder; regularization; Random forest; electroconvulsive therapy; shape analysis; classification; feature selection
• ISSN: 1945-7928; 1945-8452
• DOI: 10.1109/ISBI.2015.7163824

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 particular 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.