Sparsity-based Blind Deconvolution of Neural Activation Signal in FMRI
Hamza Cherkaoui, Thomas Moreau, Abderrahim Halimi, Philippe Ciuciu
Abstract
The estimation of the hemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is critical to deconvolve a time-resolved neural activity and get insights on the underlying cognitive processes. Existing methods propose to estimate the HRF using the experimental paradigm (EP) in task fMRI as a surrogate of neural activity. These approaches induce a bias as they do not account for latencies in the cognitive responses compared to EP and cannot be applied to resting-state data as no EP is available. In this work, we formulate the joint estimation of the HRF and neural activation signal as a semi blind deconvolution problem. Its solution can be approximated using an efficient alternate minimization algorithm. The proposed approach is applied to task fMRI data for validation purpose and compared to a state-of-the-art HRF estimation technique. Numerical experiments suggest that our approach is competitive with others while not requiring EP information.
BibTeX
@inproceedings{icassp2019_sparsitybasedbli,
title = {Sparsity-based Blind Deconvolution of Neural Activation Signal in FMRI},
author = {Hamza Cherkaoui and Thomas Moreau and Abderrahim Halimi and Philippe Ciuciu},
booktitle = {ICASSP 2019},
year = {2019}
}