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

• Tác giả: Charles D G Burns, Alessio Fracasso, Guillaume A Rousselet
• Ngôn ngữ: eng
• Ký hiệu phân loại: 343.09482 Military, defense, public property, public finance, tax, commerce (trade), industrial law
• Thông tin xuất bản: England : Scientific reports , 2025
• Bộ sưu tập: NCBI
• ID: 469213

Tóm tắt

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 implications that reproducing mass-univariate association studies requires tens-of-thousands of participants, urging researchers to adopt other methodological approaches.