2023/10/27 by Zhengxin Wang, Daniel B. Rowe, Wang, Zhengxin +5
Engineering · Medicine · Neuroscience · #Advanced MRI Techniques and Applications #Applications (stat.AP) #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Methodology (stat.ME) #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.2310.18536
openalex publication_date 2023/10/27 · openalex created_date 2023/11/01 · openalex updated_date 2026/08/01
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.