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A Bayesian General ...
A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis
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- Mejia, A. F. (author)
- Indiana University
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- Yue, Y. (author)
- Baruch College
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- Bolin, David, 1983 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper,Department of Mathematical Sciences,Chalmers tekniska högskola,Chalmers University of Technology
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- Lindgren, F. (author)
- University of Edinburgh
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- Lindquist, M. A. (author)
- Johns Hopkins University
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(creator_code:org_t)
- 2019-06-12
- 2020
- English.
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In: Journal of the American Statistical Association. - : Informa UK Limited. - 0162-1459 .- 1537-274X.
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Abstract
Subject headings
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- Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering better whole-brain visualization, dimension reduction, removal of extraneous tissue types, and improved alignment of cortical areas across subjects, it is also more compatible with common assumptions of Bayesian spatial models. However, as no spatial Bayesian model has been proposed for cs-fMRI data, most analyses continue to employ the classical general linear model (GLM), a "massive univariate" approach. Here, we propose a spatial Bayesian GLM for cs-fMRI, which employs a class of sophisticated spatial processes to model latent activation fields. We make several advances compared with existing spatial Bayesian models for volumetric fMRI. First, we use integrated nested Laplacian approximations, a highly accurate and efficient Bayesian computation technique, rather than variational Bayes. To identify regions of activation, we utilize an excursions set method based on the joint posterior distribution of the latent fields, rather than the marginal distribution at each location. Finally, we propose the first multi-subject spatial Bayesian modeling approach, which addresses a major gap in the existing literature. The methods are very computationally advantageous and are validated through simulation studies and two task fMRI studies from the Human Connectome Project. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Subject headings
- NATURVETENSKAP -- Matematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Keyword
- Bayesian smoothing
- Brain imaging
- Integrated nested Laplace approximation
- Spatial statistics
- statistical-analysis
- maps
- probability
- activation
- extent
- Mathematics
- Integrated nested Laplace approximation
Publication and Content Type
- ref (subject category)
- art (subject category)
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