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Title: Analysis of fMRI data with drift: Modified general linear model and Bayesian estimator
Authors: Luo, H.
Puthusserypady, S. 
Keywords: Bayesian estimator
Fractional noise
Functional magnetic resonance imaging (fMRI)
General linear model (GLM)
Model selection
Wavelet decomposition
Issue Date: May-2008
Citation: Luo, H., Puthusserypady, S. (2008-05). Analysis of fMRI data with drift: Modified general linear model and Bayesian estimator. IEEE Transactions on Biomedical Engineering 55 (5) : 1504-1511. ScholarBank@NUS Repository.
Abstract: The slowly varying drift poses a major problem in the analysis of functional magnetic resonance imaging (fMRI) data. In this paper, based on the observation that noise in fMRI is long memory fractional noise and the slowly varying drift resides in a subspace spanned only by large scale wavelets, we examine a modified general linear model (GLM) in wavelet domain under Bayesian framework. This modified model estimates the activation parameters at each scale of wavelet decomposition. Then, a model selection criterion based on the results from the modified scheme is proposed to model the drift. Results obtained from simulated as well as real fMRI data show that the proposed Bayesian estimator can accurately capture the noise structure, and hence, result in robust estimation of the parameters in GLM. Besides, the proposed model selection criterion works well and could efficiently remove the drift. © 2006 IEEE.
Source Title: IEEE Transactions on Biomedical Engineering
ISSN: 00189294
DOI: 10.1109/TBME.2008.918563
Appears in Collections:Staff Publications

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