PROJECT TITLE :

Nonparametric Hemodynamic Deconvolution of fMRI Using Homomorphic Filtering

ABSTRACT:

Functional magnetic resonance imaging (fMRI) is an indirect live of neural activity that is modeled as a convolution of the latent neuronal response and the hemodynamic response function (HRF). Since the sources of HRF variability will be nonneural in nature, the measured fMRI signal will not faithfully represent underlying neural activity. Thus, it's advantageous to deconvolve the HRF from the fMRI signal. But, since both latent neural activity and therefore the voxel-specific HRF is unknown, the deconvolution should be blind. Existing blind deconvolution approaches employ highly parameterized models, and it's unclear whether these models have an over fitting problem. So as to handle these issues, we one) present a nonparametric deconvolution methodology primarily based on homomorphic filtering to obtain the latent neuronal response from the fMRI signal and, a pair of) compare our approach to the simplest performing existing parametric model based mostly on the estimation of the biophysical hemodynamic model using the Cubature Kalman Filter/Smoother. We tend to 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 deconvolution models of fMRI might probably be over stated. Both simulations and experimental results demonstrate support for our hypothesis since the estimated latent neural response from both parametric and nonparametric methods were highly correlated in the visual cortex. Further, simulations showed that each methods were effective in recovering the simulated ground truth of the latent neural response.