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.

翻译：坐标元分析通过综合多项神经影像学研究证据来估计脑激活区域。在此类分析中，一个关键的实际挑战是找到一种计算高效且具有良好统计可解释性的方法来建模激活焦点的空间位置。本文提出一种生成式坐标元回归（CBMR）框架，用于近似平滑激活强度函数，并探讨研究层面协变量（例如出版年份、样本量）的影响。我们采用样条参数化方法建模脑激活的空间结构，并考虑四种随机模型来刻画焦点的随机变异。为验证CBMR的有效性，我们在20个元分析数据集上估计脑激活，进行体素级空间同质性检验，并与现有基于核的方法生成的结果进行对比。