Bayesian spatiotemporal modeling on complex-valued fMRI signals via kernel convolutions
Bayesian spatiotemporal modeling on complex-valued fMRI signals via kernel convolutions
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DOI：
10.1111/biom.13631
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发表时间：
2022-03-09
期刊：
影响因子：
1.9
通讯作者：
Rowe，Daniel
中科院分区：
文献类型：
--
作者：
Yu，Cheng-Han;Prado，Raquel;Rowe，Daniel
我们提出了一种基于模型的方法，结合了贝叶斯变量选择工具、新颖的空间核卷积结构和自回归过程，用于在复值功能磁共振成像（CV-fMRI）数据中的体素水平上检测受试者的大脑激活。利用基于核的结构的降维优势，开发了一种用于后验推理的计算高效的马尔可夫链蒙特卡罗算法。与基于高斯过程模型和其他不包含空间和/或时间结构的复值模型的替代空间方法相比，所提出的时空模型可以产生更准确的后验概率激活图和更少的误报。这在模拟数据和人类任务相关的 CV-fMRI 数据的分析中得到了说明。此外，我们还表明，复值方法主导了仅幅度方法，并且我们提出的模型中的内核结构在检测体素级别的激活时显着提高了灵敏度。
We propose a model‐based approach that combines Bayesian variable selection tools, a novel spatial kernel convolution structure, and autoregressive processes for detecting a subject's brain activation at the voxel level in complex‐valued functional magnetic resonance imaging (CV‐fMRI) data. A computationally efficient Markov chain Monte Carlo algorithm for posterior inference is developed by taking advantage of the dimension reduction of the kernel‐based structure. The proposed spatiotemporal model leads to more accurate posterior probability activation maps and less false positives than alternative spatial approaches based on Gaussian process models, and other complex‐valued models that do not incorporate spatial and/or temporal structure. This is illustrated in the analysis of simulated data and human task‐related CV‐fMRI data. In addition, we show that complex‐valued approaches dominate magnitude‐only approaches and that the kernel structure in our proposed model considerably improves sensitivity rates when detecting activation at the voxel level.