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
Source: 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.

SCOPUSTM   
Citations

9
checked on Dec 7, 2017

WEB OF SCIENCETM
Citations

8
checked on Nov 29, 2017

Page view(s)

37
checked on Dec 11, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.