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Spatial 3D Matérn Priors for Fast Whole-Brain fMRI Analysis
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
School of Mathematics, The University of Edinburgh, United Kingdom.
CEMSE Division, King Abdullah University of Science and Technology, Saudi Arabia.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.ORCID iD: 0000-0001-7061-7995
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2021 (English)In: Bayesian Analysis, ISSN 1936-0975, E-ISSN 1931-6690, Vol. 16, no 4, p. 1251-1278Article in journal (Refereed) Published
Abstract [en]

Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors has been shown to produce state-of-the-art activity maps without pre-smoothing the data. The proposed inference algorithms are computationally demanding however, and the spatial priors used have several less appealing properties, such as being improper and having infinite spatial range.We propose a statistical inference framework for whole-brain fMRI analysis based on the class of Mat ern covariance functions. The framework uses the Gaussian Markov random field (GMRF) representation of possibly anisotropic spatial Mat ern fields via the stochastic partial differential equation (SPDE) approach of Lindgren et al. (2011). This allows for more flexible and interpretable spatial priors, while maintaining the sparsity required for fast inference in the high-dimensional whole-brain setting. We develop an accelerated stochastic gradient descent (SGD) optimization algorithm for empirical Bayes (EB) inference of the spatial hyperparameters. Conditionally on the inferred hyperparameters, we make a fully Bayesian treatment of the brain activity. The Mat ern prior is applied to both simulated and experimental task-fMRI data and clearly demonstrates that it is a more reasonable choice than the previously used priors, using comparisons of activity maps, prior simulation and cross-validation.

Place, publisher, year, edition, pages
INT SOC BAYESIAN ANALYSIS , 2021. Vol. 16, no 4, p. 1251-1278
Keywords [en]
spatial priors, Gaussian Markov random fields, fMRI, spatiotemporal modeling, efficient computation
National Category
Probability Theory and Statistics Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:liu:diva-178090DOI: 10.1214/21-BA1283ISI: 000754390900008OAI: oai:DiVA.org:liu-178090DiVA, id: diva2:1582253
Funder
Swedish Research Council, 2013-5229Swedish Research Council, 2016-04187EU, Horizon 2020, 640171
Note

Funding: Swedish Research Council (Vetenskapsadet)Swedish Research Council [2013-5229, 2016-04187]; European Unions Horizon 2020 Programme for Research and Innovation [640171]; Center for Industrial Information Technology (CENIIT) at Linkoping University

Available from: 2021-07-29 Created: 2021-07-29 Last updated: 2022-03-15

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Sidén, PerEklund, AndersVillani, Mattias

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Probability Theory and StatisticsRadiology, Nuclear Medicine and Medical Imaging

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