Estimation of the Hemodynamic Response of fMRI Data Using RBF Neural Network

• Published in: IEEE transactions on biomedical engineering Vol. 54; no. 8; pp. 1371 - 1381
• Main Authors: Luo, Huaien, Puthusserypady, Sadasivan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2007; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial Intelligence; Biological neural networks; Blood; Blood Flow Velocity - physiology; Brain; Brain - blood supply; Brain - physiology; Brain Mapping - methods; Brain modeling; Cerebrovascular Circulation - physiology; Computer Simulation; Data analysis; Event-related design; functional magnetic resonance imaging (fMRI); hemodynamic response (HDR); Hemodynamics; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Models, Neurological; neural network; Neural networks; Neural Networks (Computer); Neuroimaging; Oxygen - blood; Particle measurements; radial basis functions (RBF); Studies; System identification; Volterra kernels
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2007.900795

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.