Ann Appl Stat. 2026 Mar;20(1):604-622. doi: 10.1214/25-aoas2133. Epub 2026 Mar 20.
ABSTRACT
In recent years longitudinal, multi-site imaging studies have emerged as key tools for investigating brain function. These studies follow a large number of participants for an extended period, offering exciting opportunities to uncover brain functional network changes over time as a function of clinical and demographic covariates. However, these studies also introduce many statistical challenges such as site-effects and accounting for the heterogeneous nature of network differences between subjects. Robust statistical methods are highly needed to address these issues, but to date there has been little methods development addressing these problems in the context of data-driven brain network estimation. This work addresses this gap in the literature, introducing a general Bayesian framework, REMBRAiNDT, incorporating site- and subject-effects into the network decomposition, while also enabling covariate effect estimation and efficient information pooling across brain locations. We use our procedure to conduct a novel analysis of neurodevelopment among adolescents in the longitudinal, multi-site ABCD study. We find extensive evidence of increasing functional integration with age in networks associated with higher order cognitive processes. Our study is one of the first to examine neurodevelopment using blind source separation in the longitudinal ABCD study data, and the findings enrich earlier cross-sectional results on neurodevelopment.
PMID:41878728 | PMC:PMC13008291 | DOI:10.1214/25-aoas2133
