Paper: | IMDSP-L5.4 | ||
Session: | Image and Multidimensional Signal Processing: Applications III | ||
Time: | Thursday, May 20, 16:30 - 16:50 | ||
Presentation: | Lecture | ||
Topic: | Image and Multidimensional Signal Processing: Image Formation and Computed Imaging | ||
Title: | HEMODYNAMIC TRANSFER FUNCTION ESTIMATION WITH LAGUERRE POLYNOMIALS AND CONFIDENCE INTERVALS CONSTRUCTION, FROM FUNCTIONAL MAGNETIC RESONANCE IMAGING (FMRI) DATA | ||
Authors: | Supratim Saha; Harvard University | ||
Christopher Long; Massachusetts General Hospital-NMR Center | |||
Emery Brown; Neuroscience Statistics Research Lab | |||
E. Aminoff; Massachusetts General Hospital-NMR Center | |||
M. Bar; Massachusetts General Hospital-NMR Center | |||
Victor Solo; Massachusetts General Hospital-NMR Center, United States / University of New South Wales | |||
Abstract: | In order to construct spatial activation plots from functional magnetic resonance imaging (fMRI) data, a complex spatio-temporal modeling must be solved. A crucial part of this process is the estimation of the hemodynamic response (HR) function,an impulse response relating the stimulus signal to the measured noisy response. The estimation of the HR is complicated by the presence of low frequency colored noise. The standard approach to modeling the HR is to use simple parametric models, although FIR models have been used. We offer two contributions here.Firstly we pursue a nonparametric approach usingorthonormal causal Laguerre polynomials which have become popular in the system identification literature. It also happens that the shape of the basis elements is similar to that of a typical HR. We thus expect to achieve a compact and so bias reduced and low noise representation of the HR. Additionally we develop a procedure for providing for the HR function. This feature is completely lacking in all previous work. | ||
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