AI Article Synopsis

  • Researchers in neuroscience are using Big Data to improve the reliability and replication of cognitive studies, focusing on memory testing.
  • They conducted a mega-analysis with data from 53 studies, involving over 10,500 individuals, employing methods to harmonize data and reduce variability across different sites.
  • Their findings show that large-scale data sharing can enhance the reproducibility of research in behavioral sciences, and they offer a free conversion tool for this purpose.

Article Abstract

Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample 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 individuals. 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 individual'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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9882238PMC
http://dx.doi.org/10.1101/2023.01.16.524331DOI Listing

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