Removal of Scanner Effects in Covariance Improves Multivariate Pattern Analysis in Neuroimaging Data

• Activity Number: 471 - Contemporary Statistical Methods for Imaging Data Analysis
• Type: Contributed
• Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Andrew Chen* and Haochang Shou and Russell Shinohara and Philip Cook and Nicholas J Tustison and Joanne Beer
• Companies: University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and University of Virginia and University of Pennsylvania
• Keywords: harmonization; neuroimaging; multi-site analysis; site effect; scanner effect; covariance

Abstract

As multi-site neuroimaging studies become commonplace, harmonization methods have been developed to remove scanner-related effects in the mean and variance of measurements. Contemporaneously, the use of multivariate pattern analysis has also increased. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that the currently available methods for removing scanner effects are inherently insufficient for MVPA. This stems from the fact that no currently available harmonization approach has addressed how correlations between measurements can vary across scanners. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) is used to show that considerable differences in covariance exist across scanners and that the state-of-the-art harmonization techniques do not address this issue. We also propose a novel methodology that harmonizes covariance of multivariate image measurements across scanners and demonstrate its improved performance in data harmonization, which further facilitates more power for detection of clinical associations.