Publication
Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium
Publisher:
Cold Spring Harbor Laboratory
Date:
19-01-2023
DOI:
10.1101/2023.01.16.524331
Abstract: Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing s le sizes, statistical power, and representativeness of data. These efforts unveil new questions about integrating data arising from distinct sources and instruments. We focus on the most frequently assessed cognitive domain - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated global raw data from 53 studies totaling N = 10,505 in iduals. A mega-analysis was conducted using empirical bayes harmonization to remove site effects, followed by linear models adjusting for common covariates. A continuous item response theory (IRT) model estimated each in idual’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance while preserving covariate effects, and our conversion tool is freely available online. This demonstrates that large-scale data sharing and harmonization initiatives can address reproducibility and integration challenges across the behavioral sciences. We present a global effort to devise harmonization procedures necessary to meaningfully leverage big data.