Dec 23, 2014

Nonparametric hemodynamic deconvolution of FMRI using homomorphic filtering

IEEE Transactions on Medical Imaging
Karthik Ramakrishnan SreenivasanGopikrishna Deshpande

Abstract

Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity which is modeled as a convolution of the latent neuronal response and the hemodynamic response function (HRF). Since the sources of HRF variability can be nonneural in nature, the measured fMRI signal does not faithfully represent underlying neural activity. Therefore, it is advantageous to deconvolve the HRF from the fMRI signal. However, since both latent neural activity and the voxel-specific HRF is unknown, the deconvolution must be blind. Existing blind deconvolution approaches employ highly parameterized models, and it is unclear whether these models have an over fitting problem. In order to address these issues, we 1) present a nonparametric deconvolution method based on homomorphic filtering to obtain the latent neuronal response from the fMRI signal and, 2) compare our approach to the best performing existing parametric model based on the estimation of the biophysical hemodynamic model using the Cubature Kalman Filter/Smoother. We hypothesized that if the results from nonparametric deconvolution closely resembled that obtained from parametric deconvolution, then the problem of over fitting during estimation in highly parameterized de...Continue Reading

  • References57
  • Citations13

References

  • References57
  • Citations13

Mentioned in this Paper

Biophysics
Neuronal
FMRI
Entire Brodmann Areas 17 (Striate Cortex),18 (Parastriate Cortex) and 19 (Peristriate Cortex) of Occipital Lobe
Hemodynamics
Visual Cortex

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