Machine learning for clinical diagnosis from functional magnetic resonance imaging

• Published in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 1211 - 1217 vol. 1
• Main Authors: Lei Zhang, Samaras, D., Tomasi, D., Volkow, N., Goldstein, R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Boosting; Brain; Clinical diagnosis; Humans; Information analysis; Learning systems; Machine learning; Machine learning algorithms; Magnetic analysis; Magnetic resonance imaging
• ISBN: 0769523722; 9780769523729
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2005.219

Functional magnetic resonance imaging (fMRI) has enabled scientists to look into the active human brain. FMRI provides a sequence of 3D brain images with intensities representing brain activations. Standard techniques for fMRI analysis traditionally focused on finding the area of most significant brain activation for different sensations or activities. In this paper, we explore a new application of machine learning methods to a more challenging problem: classifying subjects into groups based on the observed 3D brain images when the subjects are performing the same task. Here we address the separation of drug-addicted subjects from healthy non-drug-using controls. In this paper, we explore a number of classification approaches. We introduce a novel algorithm that integrates side information into the use of boosting. Our algorithm clearly outperformed well-established classifiers as documented in extensive experimental results. This is the first time that machine learning techniques based on 3D brain images are applied to a clinical diagnosis that currently is only performed through patient self-report. Our tools can therefore provide information not addressed by traditional analysis methods and substantially improve diagnosis.