Efficient fully Bayesian approach to brain activity mapping with complex-valued fMRI data

Zhengxin Wang, Daniel B. Rowe, Xinyi Li and D. Andrew Brown

Journal of Applied Statistics, 2025, vol. 52, issue 6, 1299-1314

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. 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.

Date: 2025
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DOI: 10.1080/02664763.2024.2422392

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