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首页> 外文期刊>Journal of the royal statistical society >Bayesian analysis of functional magnetic resonance imaging data with spatially varying auto-regressive orders
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Bayesian analysis of functional magnetic resonance imaging data with spatially varying auto-regressive orders

机译：具有空间变化的自回归阶的功能磁共振成像数据的贝叶斯分析

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摘要

Statistical modelling of functional magnetic resonance imaging data is challenging as the data are both spatially and temporally correlated. Spatially, measurements are taken at thousands of contiguous regions, called voxels, and temporally measurements are taken at hundreds of time points at each voxel. Recent advances in Bayesian hierarchical modelling have addressed the challenges of spatiotemporal structure in functional magnetic resonance imaging data with models incorporating both spatial and temporal priors for signal and noise. Whereas there has been extensive research on modelling the functional magnetic resonance imaging signal (i.e. the convolution of the experimental design with the functional choice for the haemodynamic response function) and its spatial variability, less attention has been paid to realistic modelling of the temporal dependence that typically exists within the functional magnetic resonance imaging noise, where a low order auto-regressive process is typically adopted. Furthermore, the auto-regressive order is held constant across voxels (e.g. AR(1) at each voxel). Motivated by an event-related functional magnetic resonance imaging experiment, we propose a novel hierarchical Bayesian model with automatic selection of the auto-regressive orders of the noise process that vary spatially over the brain. With simulation studies we show that our model is more statistically efficient and we apply it to our motivating example.
机译：功能性磁共振成像数据的统计建模具有挑战性，因为数据在空间和时间上都相关。在空间上，在称为体素的数千个连续区域进行测量，并且在每个体素的数百个时间点进行时间测量。贝叶斯分层建模的最新进展已经解决了功能磁共振成像数据中时空结构的挑战，该模型采用了包含信号和噪声的时空先验的模型。尽管对功能磁共振成像信号（即血液动力学响应函数的功能选择与实验设计的卷积）及其空间可变性进行建模的研究已经广泛开展，但对时间依赖性的现实建模却很少关注通常在功能性磁共振成像噪声中存在，其中通常采用低阶自回归过程。此外，自回归顺序在体素之间保持恒定（例如，每个体素处的AR（1））。受事件相关的功能磁共振成像实验的启发，我们提出了一种新颖的分层贝叶斯模型，该模型可以自动选择在大脑中空间变化的噪声过程的自回归阶数。通过仿真研究，我们证明了我们的模型在统计上更有效，并且将其应用于了激励性示例。

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