Bias in data-driven replicability analysis of univariate brain-wide association studies.

ABSTRACT: Recent studies have used big neuroimaging datasets to answer an important question: how many subjects are required for reproducible brain-wide association studies? These data-driven approaches could be considered a framework for testing the reproducibility of several neuroimaging models and measures. Here we test part of this framework, namely estimates of statistical errors of univariate brain-behaviour associations obtained from resampling large datasets with replacement. We demonstrate that reported estimates of statistical errors are largely a consequence of bias introduced by random effects when sampling with replacement close to the full sample size. We show that future meta-analyses can largely avoid these biases by only resampling up to 10% of the full sample size. We discuss impli

SUBMITTER: Burns CDG 

PROVIDER: S-EPMC11840108 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature