DocumentCode :
1056011
Title :
Estimation of the Hemodynamic Response of fMRI Data Using RBF Neural Network
Author :
Luo, Huaien ; Puthusserypady, Sadasivan
Author_Institution :
Nat. Univ. of Singapore
Volume :
54
Issue :
8
fYear :
2007
Firstpage :
1371
Lastpage :
1381
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.
Keywords :
biomedical MRI; brain; haemodynamics; radial basis function networks; Balloon model; Volterra series method; blood oxygenation level-dependent signals; brain regions; data analysis; functional magnetic resonance imaging; hemodynamic response; neuroimaging; radial basis function neural network; Biological neural networks; Blood; Brain modeling; Data analysis; Hemodynamics; Magnetic resonance imaging; Neural networks; Neuroimaging; Particle measurements; System identification; Event-related design; Volterra kernels; functional magnetic resonance imaging (fMRI); hemodynamic response (HDR); neural network; radial basis functions (RBF); Artificial Intelligence; Blood Flow Velocity; Brain; Brain Mapping; Cerebrovascular Circulation; Computer Simulation; Humans; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Models, Neurological; Neural Networks (Computer); Oxygen;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2007.900795
Filename :
4273611
Link To Document :
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