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Title: fMRI data analysis with nonstationary noise models: A Bayesian approach
Authors: Luo, H.
Puthusserypady, S. 
Keywords: Bayesian estimator
Fractional noise
Functional magnetic resonance imaging (fMRI)
General linear model (GLM)
Receiver operating characteristic (ROC) curve
Wavelet transform
Issue Date: Sep-2007
Citation: Luo, H., Puthusserypady, S. (2007-09). fMRI data analysis with nonstationary noise models: A Bayesian approach. IEEE Transactions on Biomedical Engineering 54 (9) : 1621-1630. ScholarBank@NUS Repository.
Abstract: The assumption of noise stationarity in the functional magnetic resonance imaging (fMRI) data analysis may lead to the loss of crucial dynamic features of the data and thus result in inaccurate activation detection. In this paper, a Bayesian approach is proposed to analyze the fMRI data with two nonstationary noise models (the time-varying variance noise model and the fractional noise model). The covariance matrices of the time-varying variance noise and the fractional noise after wavelet transform are diagonal matrices. This property is investigated under the Bayesian framework. The Bayesian estimator not only gives an accurate estimate of the weights in general linear model, but also provides posterior probability of activation in a voxel and, hence, avoids the limitations (i.e., using only hypothesis testing) in the classical methods. The performance of the proposed Bayesian methods (under the assumption of different noise models) are compared with the ordinary least squares (OLS) and the weighted least squares (WLS) methods. Results from the simulation studies validate the superiority of the proposed approach to the OLS and WLS methods considering the complex noise structures in the fMRI data. © 2007 IEEE.
Source Title: IEEE Transactions on Biomedical Engineering
ISSN: 00189294
DOI: 10.1109/TBME.2007.902591
Appears in Collections:Staff Publications

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