Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. These signals, composed of magnitude and phase, offer a rich source of information for understanding brain functions. Traditional fMRI analyses have largely focused on magnitude information, often overlooking the potential insights offered by phase data. In this paper, we propose a novel fully Bayesian model designed for analyzing single-subject complex-valued fMRI (cv-fMRI) data. Our model, which we refer to as the CV-M&P model, is distinctive in its comprehensive utilization of both magnitude and phase information in fMRI signals, allowing for independent prediction of different types of activation maps. We incorporate Gaussian Markov random fields (GMRFs) to capture spatial correlations within the data, and employ image partitioning and parallel computation to enhance computational efficiency. Our model is rigorously tested through simulation studies, and then applied to a real dataset from a unilateral finger-tapping experiment. The results demonstrate the model's effectiveness in accurately identifying brain regions activated in response to specific tasks, distinguishing between magnitude and phase activation.

翻译：功能性磁共振成像（fMRI）在神经影像学中发挥着关键作用，能够通过复值信号探索大脑活动。这些由幅度和相位组成的信号为理解脑功能提供了丰富的信息来源。传统fMRI分析主要集中于幅度信息，往往忽视了相位数据所提供的潜在洞见。本文提出了一种全新的完全贝叶斯模型，专门用于分析单被试复值fMRI（cv-fMRI）数据。我们将该模型称为CV-M&P模型，其独特之处在于综合利用fMRI信号中的幅度和相位信息，能够独立预测不同类型的激活图。我们引入高斯马尔可夫随机场（GMRF）以捕捉数据中的空间相关性，并采用图像分区与并行计算技术提升计算效率。该模型通过仿真研究进行了严格测试，随后应用于单侧手指敲击实验的真实数据集。结果表明，该模型能有效准确识别响应特定任务激活的脑区，并区分幅度与相位激活模式。