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Bayesian Rician Regression for Neuroimaging
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.ORCID iD: 0000-0003-2193-6003
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Faculty of Arts and Sciences. 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. Linköping University, Faculty of Arts and Sciences.
2017 (English)In: Frontiers in Neuroscience, ISSN 1662-4548, E-ISSN 1662-453X, Vol. 11, 586Article in journal (Refereed) Published
Abstract [en]

It is well-known that data from diffusion weighted imaging (DWI) follow the Rician distribution. The Rician distribution is also relevant for functional magnetic resonance imaging (fMRI) data obtained at high temporal or spatial resolution. We propose a general regression model for non-central chi (NC-chi) distributed data, with the heteroscedastic Rician regression model as a prominent special case. The model allows both parameters in the Rician distribution to be linked to explanatory variables, with the relevant variables chosen by Bayesian variable selection. A highly efficient Markov chain Monte Carlo (MCMC) algorithm is proposed to capture full model uncertainty by simulating from the joint posterior distribution of all model parameters and the binary variable selection indicators. Simulated regression data is used to demonstrate that the Rician model is able to detect the signal much more accurately than the traditionally used Gaussian model at low signal-to-noise ratios. Using a diffusion dataset from the Human Connectome Project, it is also shown that the commonly used approximate Gaussian noise model underestimates the mean diffusivity (MD) and the fractional anisotropy (FA) in the single-diffusion tensor model compared to the Rician model.

Place, publisher, year, edition, pages
FRONTIERS MEDIA SA , 2017. Vol. 11, 586
Keyword [en]
DTI; diffusion; fMRI; fractional anisotropy; mean diffusivity; MCMC; Rician
National Category
Psychology (excluding Applied Psychology)
Identifiers
URN: urn:nbn:se:liu:diva-142834DOI: 10.3389/fnins.2017.00586ISI: 000413325900002OAI: oai:DiVA.org:liu-142834DiVA: diva2:1154936
Note

Funding Agencies|Swedish research council [2013-5229, 2015-05356]; Information Technology for European Advancement (ITEA) 3 Project BENEFIT; National Institute of Dental and Craniofacial Research (NIDCR); National Institute of Mental Health (NIMH); National Institute of Neurological Disorders and Stroke (NINDS)

See also https://doi.org/10.1101/095844, BioRxvi.org

Available from: 2017-11-06 Created: 2017-11-06 Last updated: 2017-12-07

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Wegmann, BertilEklund, AndersVillani, Mattias
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The Division of Statistics and Machine LearningFaculty of Arts and SciencesDivision of Biomedical EngineeringFaculty of Science & EngineeringCenter for Medical Image Science and Visualization (CMIV)
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