Please use this identifier to cite or link to this item:
https://doi.org/10.1109/TBME.2007.900795
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. https://doi.org/10.1109/TBME.2007.900795 | 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 | URI: | http://scholarbank.nus.edu.sg/handle/10635/55911 | ISSN: | 00189294 | DOI: | 10.1109/TBME.2007.900795 |
Appears in Collections: | Staff Publications |
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.