Neuroimaging meta regression for coordinate based meta analysis data with a spatial model.

ABSTRACT: Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-regression (CBMR) framework to approximate a smooth activation intensity function and investigate the effect of study-level covariates (e.g. year of publication, sample size). We employ a spline parameterization to model the spatial structure of brain activation and consider four stochastic models for modeling the random variation in foci. To examine the validity of CBMR, we estimate brain activation on 20 meta-analytic datasets, conduct

SUBMITTER: Yu Y 

PROVIDER: S-EPMC11471956 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature