Neuroradiologists and neurosurgeons increasingly opt to use functional magnetic resonance imaging (fMRI) to map functionally relevant brain regions for noninvasive presurgical planning and intraoperative neuronavigation. This application requires a high degree of spatial accuracy, but the fMRI signal-to-noise ratio (SNR) decreases as spatial resolution increases. In practice, fMRI scans can be collected at multiple spatial resolutions, and it is of interest to make more accurate inference on brain activity by combining data with different resolutions. To this end, we develop a new Bayesian model to leverage both better anatomical precision in high resolution fMRI and higher SNR in standard resolution fMRI. We assign a Gaussian process prior to the mean intensity function and develop an efficient, scalable posterior computation algorithm to integrate both sources of data. We draw posterior samples using an algorithm analogous to Riemann manifold Hamiltonian Monte Carlo in an expanded parameter space. We illustrate our method in analysis of presurgical fMRI data, and show in simulation that it infers the mean intensity more accurately than alternatives that use either the high or standard resolution fMRI data alone.

翻译：神经放射科医生和神经外科医生越来越倾向于使用功能性磁共振成像（fMRI）来绘制功能相关的脑区，用于无创术前规划和术中神经导航。这一应用需要高度的空间精度，但fMRI的信噪比（SNR）会随着空间分辨率的提高而降低。在实践中，fMRI扫描可以在多种空间分辨率下采集，因此通过结合不同分辨率的数据对脑活动进行更准确的推断具有重要意义。为此，我们开发了一种新的贝叶斯模型，以充分利用高分辨率fMRI更好的解剖精度和标准分辨率fMRI更高的信噪比。我们为均值强度函数赋予高斯过程先验，并开发了一种高效、可扩展的后验计算算法以整合这两种数据源。我们使用类似于黎曼流形哈密顿蒙特卡洛的算法在扩展的参数空间中抽取后验样本。我们在术前fMRI数据分析中展示了该方法，并通过模拟表明，相比单独使用高分辨率或标准分辨率fMRI数据的替代方法，该方法能更准确地推断均值强度。