Working memory (WM) was one of the first cognitive processes studied with functional magnetic resonance imaging (fMRI). With now over 20 years of studies on WM, each study with tiny sample sizes, there is a need for meta-analysis to identify the brain regions consistently activated by WM tasks, and to understand the inter-study variation in those activations. However, current methods in the field cannot fully account for the spatial nature of neuroimaging meta-analysis data or the heterogeneity observed among WM studies. In this work, we propose a fully Bayesian random-effects meta-regression model based on log-Gaussian Cox processes, which can be used for meta-analysis of neuroimaging studies. An efficient MCMC scheme for posterior simulations is presented which makes use of some recent advances in parallel computing using graphics processing units (GPUs). Application of the proposed model to a real dataset provides valuable insights regarding the function of the WM.

中文翻译：

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贝叶斯对数高斯 Cox 过程回归：应用于神经影像工作记忆研究的荟萃分析。

工作记忆（WM）是最早用功能磁共振成像（fMRI）研究的认知过程之一。目前 WM 的研究已超过 20 年，每项研究的样本量都很小，因此需要进行荟萃分析来识别 WM 任务持续激活的大脑区域，并了解这些激活的研究间差异。然而，该领域当前的方法无法完全解释神经影像荟萃分析数据的空间性质或 WM 研究中观察到的异质性。在这项工作中，我们提出了一种基于对数高斯 Cox 过程的完全贝叶斯随机效应元回归模型，可用于神经影像学研究的元分析。提出了一种用于后验模拟的高效 MCMC 方案，该方案利用了使用图形处理单元 (GPU) 的并行计算的一些最新进展。将所提出的模型应用于真实数据集可以提供有关 WM 功能的宝贵见解。