Lee Kuo-Jung, Jones Galin L, Caffo Brian S, Bassett Susan Spear
Department of Statistics, National Cheng Kung University.
School of Statistics, University of Minnesota.
Bayesian Anal. 2014;9(3):699-732. doi: 10.1214/14-BA873.
A common objective of fMRI (functional magnetic resonance imaging) studies is to determine subject-specific areas of increased blood oxygenation level dependent (BOLD) signal contrast in response to a stimulus or task, and hence to infer regional neuronal activity. We posit and investigate a Bayesian approach that incorporates spatial and temporal dependence and allows for the task-related change in the BOLD signal to change dynamically over the scanning session. In this way, our model accounts for potential learning effects in addition to other mechanisms of temporal drift in task-related signals. We study the properties of the model through its performance on simulated and real data sets.
功能磁共振成像（fMRI）研究的一个常见目标是确定受试者特定的区域，这些区域在对刺激或任务做出反应时，血液氧合水平依赖（BOLD）信号对比度增加，从而推断区域神经元活动。我们提出并研究了一种贝叶斯方法，该方法纳入了空间和时间依赖性，并允许BOLD信号中与任务相关的变化在扫描过程中动态变化。通过这种方式，我们的模型除了考虑任务相关信号中时间漂移的其他机制外，还考虑了潜在的学习效应。我们通过该模型在模拟数据集和真实数据集上的表现来研究其特性。
