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:神经放射科和神经外科医生越来越多地选择使用功能性磁共振成像(fMRI)绘制与功能相关的脑区域图,用于非侵入性前外科规划和内科神经导航。这种应用需要高度的空间精确度,但FMRI信号对噪音比(SNR)随着空间分辨率的提高而下降。在实践中,FMRI扫描可以在多个空间分辨率上采集,并且有兴趣通过将数据与不同分辨率合并,更准确地推断大脑活动。为此,我们开发了一个新的巴伊西亚模型,以利用高分辨率FMRI的解剖精度和标准分辨率FMRI的更高SNR。我们在平均强度功能之前指定一个高斯进程,并开发一个高效、可伸缩的远地点计算算法,以整合两个数据源。我们在扩大的参数空间中使用类似于Riemann多个汉密尔顿·蒙特卡洛的算法,我们演示了我们分析前科FMRI数据的方法,并在模拟中显示,高分辨率比标准替代品更精确的分辨率高。