Relating Multi-Sequence Time Series Data from MS Lesions on Structural MRI to Clinical Covariates and Outcomes

• Activity Number: 162
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Elizabeth Sweeney* and Russell Shinohara and Blake Dewey and Matthew Schindler and Kennon Copeland and Daniel Reich and Ciprian Crainiceanu and Ani Eloyan
• Companies: The Johns Hopkins University and University of Pennsylvania and National Institute of Neurological Disorders and Stroke and NIH and NORC at the University of Chicago and NIH and The Johns Hopkins University and The Johns Hopkins University
• Keywords: Multiple Sclerosis ; Longtiduinal Data ; Strucutral Magnetic Resonance Imaging

Abstract

Structural magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients. The formation of these lesions is a complex sequence of inflammation, degeneration, and repair that MRI has been shown to be sensitive to. We characterize the lesion formation process with multi-sequence structural MRI. We have longitudinal MRI from 60 MS patients, each with between 10 and 40 studies. Each study consists of a T1-weighted, T2-weighted, fluid attenuated inversion recovery (FLAIR) and proton density (PD) volume. We extract the multi- sequence time series of the voxel intensities from the four volumes and use functional principal component analysis to identify voxels that contain permanent damage and repair. We then investigate this repair and permanent damage in relation to clinical covariates such as disease duration, MS subtype, Expanded Stability Status Score (EDSS), and treatment.