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

Content Provider	IEEE Xplore Digital Library
Author	Huaien Luo Puthusserypady, S.
Copyright Year	1964
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.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	11
File Size	1058318
Starting Page	1371
Ending Page	1381
File Format	PDF
ISSN	00189294
Volume Number	54
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hemodynamics Neural networks Biological neural networks Brain modeling Magnetic resonance imaging Neuroimaging System identification Data analysis Particle measurements Blood Volterra kernels Event-related design functional magnetic resonance imaging (fMRI) hemodynamic response (HDR) neural network radial basis functions (RBF)
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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