A1567
Title: Efficient fully Bayesian approach to brain activity mapping with complex-valued fMRI data
Authors: Andrew Brown - Clemson University (United States) [presenting]
Abstract: 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. A fully Bayesian model for brain activity mapping is proposed with cv-fMRI data. The model accommodates temporal and spatial dynamics. Additionally, a computationally efficient sampling algorithm is proposed, which enhances processing speed through image partitioning. The approach is shown to be computationally efficient via image partitioning and parallel computation while being competitive with state-of-the-art methods. These claims are supported by both simulated numerical studies and an application to real cv-fMRI data obtained from a finger-tapping experiment.
