A fully Bayesian approach for comprehensive mapping of magnitude and phase brain activation in complex-valued fMRI data

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 analyse...

Full description

Saved in:
Bibliographic Details
Published in:Magnetic resonance imaging Vol. 109; pp. 271 - 285
Main Authors: Wang, Zhengxin, Rowe, Daniel B., Li, Xinyi, Brown, D. Andrew
Format: Journal Article
Language:English
Published: Netherlands Elsevier Inc 01.06.2024
Subjects:
Bayes Theorem
Brain - diagnostic imaging
Brain - physiology
Brain Mapping - methods
Computer Simulation
Gibbs sampling
Magnetic Resonance Imaging - methods
Parallel computation
Phase analysis
Rowe–Logan
Spike and slab prior
Variable selection
Spike and slab prior
Parallel computation
Gibbs sampling
Phase analysis
Rowe–Logan
Variable selection
ISSN:0730-725X, 1873-5894, 1873-5894
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. •Most fMRI studies use real-valued, magnitude only data, ignoring information in the complex part.•Identifying changes in magnitude and phase allows for detection of meaningful neuronal activity that would be missed by studying magnitude alone.•We propose a fully Bayesian model for identifying magnitude and phase changes in task-based BOLD fMRI.•The model accounts for spatial association via sparse Gaussian Markov random fields and is computationally scalable via parcellation.•We demonstrate that the model is able to not only identify task-related changes in BOLD signal, but the type of change (magnitude, phase, or both).
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Author Statement
The authors of the manuscript titled, “A fully Bayesian approach for comprehensive mapping of magnitude and phase brain activation in complex-valued fMRI data,” hereby declare no competing interests.
ISSN:0730-725X
1873-5894
1873-5894
DOI:10.1016/j.mri.2024.03.029