  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 smooth activation intensity function and investigate
the effect of study-level covariates (e.g., year of publication, sample size).
We employ spline parameterization to model spatial structure of brain
activation and consider four stochastic models for modelling the random
variation in foci. To examine the validity of CBMR, we estimate brain
activation on $20$ meta-analytic datasets, conduct spatial homogeneity tests at
voxel level, and compare to results generated by existing kernel-based
approaches.
