Efficient Fully Bayesian Approach to Brain Activity Mapping with Complex-Valued fMRI Data
Document Type
Article
Publication Date
5-2025
Publisher
Taylor & Francis
Source Publication
Journal of Applied Statistics
Source ISSN
0266-4763
Original Item ID
DOI: 10.1080/02664763.2024.2422392
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.
Recommended Citation
Wang, Zhengxin; Rowe, Daniel B.; Li, Xinyi; and Brown, D. Andrew, "Efficient Fully Bayesian Approach to Brain Activity Mapping with Complex-Valued fMRI Data" (2025). Mathematical and Statistical Science Faculty Research and Publications. 158.
https://epublications.marquette.edu/math_fac/158
Comments
Journal of Applied Statistics, Vol. 52, No. 6 (May 2025): 1299-1314. DOI.