Mitigating site effects in covariance for machine learning in neuroimaging data.

ABSTRACT: To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site-related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi-center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint re

SUBMITTER: Chen AA 

PROVIDER: S-EPMC8837590 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature