Functional magnetic resonance imaging (fMRI) enables indirect detection of brain activity changes via the blood-oxygen-level-dependent (BOLD) signal. Conventional analysis methods mainly rely on the real-valued magnitude of these signals. In contrast, research suggests that analyzing both real and imaginary components of the complex-valued fMRI (cv-fMRI) signal provides a more holistic approach that can increase power to detect neuronal activation. We propose a fully Bayesian model for brain activity mapping with cv-fMRI data. Our model accommodates temporal and spatial dynamics. Additionally, we propose a computationally efficient sampling algorithm, which enhances processing speed through image partitioning. Our approach is shown to be computationally efficient via image partitioning and parallel computation while being competitive with state-of-the-art methods. We support these claims with both simulated numerical studies and an application to real cv-fMRI data obtained from a finger-tapping experiment.

翻译：功能磁共振成像（fMRI）通过血氧水平依赖（BOLD）信号间接检测脑活动变化。传统分析方法主要依赖这些信号的实值幅度。相比之下，研究表明，分析复数fMRI（cv-fMRI）信号的实部和虚部可提供更全面的方法，能够增强神经元活动检测的统计效能。我们提出了一种适用于cv-fMRI数据的全贝叶斯脑活动映射模型。该模型兼顾了时间与空间动态特性。此外，我们设计了一种计算高效的采样算法，通过图像分区提高了处理速度。实验表明，该方法在图像分区和并行计算方面具有计算效率优势，同时与现有最优方法性能相当。我们通过数值模拟研究及一项基于手指敲击实验的真实cv-fMRI数据应用验证了上述结论。