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Title: Estimation of the hemodynamic response of fMRI data using RBF neural network
Authors: Luo, H.
Puthusserypady, S. 
Keywords: Event-related design
Functional magnetic resonance imaging (fMRI)
Hemodynamic response (HDR)
Neural network
Radial basis functions (RBF)
Volterra kernels
Issue Date: Aug-2007
Citation: Luo, H., Puthusserypady, S. (2007-08). Estimation of the hemodynamic response of fMRI data using RBF neural network. IEEE Transactions on Biomedical Engineering 54 (8) : 1371-1381. ScholarBank@NUS Repository.
Abstract: Functional magnetic resonance imaging (fMRI) is an important technique for neuroimaging. The conventional system identification methods used in fMRI data analysis assume a linear time-invariant system with the impulse response described by the hemodynamic responses (HDR). However, the measured blood oxygenation level-dependent (BOLD) signals to a particular processing task (for example, rapid event-related fMRI design) show nonlinear properties and vary with different brain regions and subjects. In this paper, radial basis function (RBF) neural network (a powerful technique for modelling nonlinearities) is proposed to model the dynamics underlying the fMRI data. The equivalence of the proposed method to the existing Volterra series method has been demonstrated. It is shown that the first- and second-order Volterra kernels could be deduced from the parameters of the RBF neural network. Studies on both simulated (using Balloon model) as well as real event-related fMRI data show that the proposed method can accurately estimate the HDR of the brain and capture the variations of the HDRs as a function of the brain regions and subjects. © 2007 IEEE.
Source Title: IEEE Transactions on Biomedical Engineering
ISSN: 00189294
DOI: 10.1109/TBME.2007.900795
Appears in Collections:Staff Publications

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