Srikanth, R and Ramakrshnan, AG (2006) Wavelet-based estimation of hemodynamic response function from fMRI data. In: International Journal Of Neural Systems, 16 (2). pp. 125-138.Full text not available from this repository.
We present a new algorithm to estimate hemodynamic response function (HRF) and drift components of fMRI data in wavelet domain. The HRF is modeled by both parametric and nonparametric models. The functional Magnetic resonance Image (fMRI) noise is modeled as a fractional brownian motion (fBm). The HRF parameters are estimated in wavelet domain by exploiting the property that wavelet transforms with a sufficient number of vanishing moments decorrelates a fBm process. Using this property, the noise covariance matrix in wavelet domain can be assumed to be diagonal whose entries are estimated using the sample variance estimator at each scale. We study the influence of the sampling rate of fMRI time series and shape assumption of HRF on the estimation performance. Results are presented by adding synthetic HRFs on simulated and null fMRI data. We also compare these methods with an existing method,(1) where correlated fMRI noise is modeled by a second order polynomial functions.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to World Scientific Publishing Company.|
|Keywords:||Computer Science,Artificial Intelligence;Bayesian estimation; wavelets;fMRI;HRF modeling; 1 / f noise|
|Department/Centre:||Division of Electrical Sciences > Electrical Engineering|
|Date Deposited:||21 Feb 2010 11:53|
|Last Modified:||21 Feb 2010 11:53|
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