Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium

Area of Science:

• Neuroscience
• Cognitive Science
• Behavioral Science

Background:

• Replication and reliability issues in neuroscience research necessitate Big Data approaches.
• Integrating data from diverse sources and instruments presents significant challenges.
• Memory testing is a frequently assessed cognitive domain crucial for understanding brain function.

Approach:

• Aggregated global raw data from 53 studies (N = 10,505 individuals).
• Conducted a mega-analysis using empirical Bayes harmonization to mitigate site effects.
• Employed a continuous item response theory (IRT) model to estimate latent verbal learning ability, accounting for item difficulties.

Key Points:

• Harmonization significantly reduced inter-site variance while preserving covariate effects.
• The developed data harmonization process is reliable across common memory measures.
• A conversion tool for this harmonization process is available online.

Conclusions:

• Large-scale data sharing and harmonization initiatives can effectively address reproducibility challenges.
• This approach facilitates data integration across the behavioral sciences.
• The study demonstrates a viable method for harmonizing cognitive data for Big Data research.