Bridging big data in the ENIGMA consortium to combine non-equivalent cognitive measures.

ABSTRACT: Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample size. These efforts unveil new questions about how to integrate data across distinct sources and instruments. The goal of this study was to link scores across common auditory verbal learning tasks (AVLTs). This international secondary analysis aggregated multisite raw data for AVLTs across 53 studies totaling 10,505 individuals. Using the ComBat-GAM algorithm, we isolated and removed the component of memory scores associated with site effects while preserving instrumental effects. After adjustment, a continuous item response theory model used multiple memory items of varying difficulty to estimate each individual's latent verbal learning ability on a single scale. Equiva

SUBMITTER: Kennedy E 

PROVIDER: S-EPMC11484938 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature