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...

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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
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Abstract	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).
AbstractList	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. 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.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. 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).
Author	Wang, Zhengxin Brown, D. Andrew Rowe, Daniel B. Li, Xinyi
AuthorAffiliation	b Department of Mathematical and Statistical Sciences, Marquette University, Milwaukee, 53233, Wisconsin, U.S.A a School of Mathematical and Statistical Sciences, Clemson University, Clemson, 29634, South Carolina, U.S.A
AuthorAffiliation_xml	– name: b Department of Mathematical and Statistical Sciences, Marquette University, Milwaukee, 53233, Wisconsin, U.S.A – name: a School of Mathematical and Statistical Sciences, Clemson University, Clemson, 29634, South Carolina, U.S.A
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Keywords	Spike and slab prior Parallel computation Gibbs sampling Phase analysis Rowe–Logan Variable selection
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Notes	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.
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Snippet	Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals....
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SubjectTerms	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
Title	A fully Bayesian approach for comprehensive mapping of magnitude and phase brain activation in complex-valued fMRI data
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