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A Bayesian Heteroscedastic GLM with Application to fMRI Data with Motion Spikes
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering. Linköping University, Faculty of Arts and Sciences.
Department of Biostatistics, Johns Hopkins University, Baltimore, USA.
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
2017 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572Article in journal (Refereed) Epub ahead of print
Abstract [en]

We propose a voxel-wise general linear model with autoregressive noise and heteroscedastic noise innovations (GLMH) for analyzing functional magnetic resonance imaging (fMRI) data. The model is analyzed from a Bayesian perspective and has the benefit of automatically down-weighting time points close to motion spikes in a data-driven manner. We develop a highly efficient Markov Chain Monte Carlo (MCMC) algorithm that allows for Bayesian variable selection among the regressors to model both the mean (i.e., the design matrix) and variance. This makes it possible to include a broad range of explanatory variables in both the mean and variance (e.g., time trends, activation stimuli, head motion parameters and their temporal derivatives), and to compute the posterior probability of inclusion from the MCMC output. Variable selection is also applied to the lags in the autoregressive noise process, making it possible to infer the lag order from the data simultaneously with all other model parameters. We use both simulated data and real fMRI data from OpenfMRI to illustrate the importance of proper modeling of heteroscedasticity in fMRI data analysis. Our results show that the GLMH tends to detect more brain activity, compared to its homoscedastic counterpart, by allowing the variance to change over time depending on the degree of head motion.

Place, publisher, year, edition, pages
Elsevier, 2017.
Keyword [en]
Bayesian; FMRI; Heteroscedastic; MCMC; Head motion; Motion spikes
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-137055DOI: 10.1016/j.neuroimage.2017.04.069OAI: oai:DiVA.org:liu-137055DiVA: diva2:1092501
Available from: 2017-05-03 Created: 2017-05-03 Last updated: 2017-05-11

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The full text will be freely available from 2018-05-01 10:46
Available from 2018-05-01 10:46

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Eklund, AndersVillani, Mattias
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Citation style
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