Sparsity-based blind deconvolution of neural activation signal in fMRI

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 laten-cies 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 neu-ral 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.
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Hamza Cherkaoui, Thomas Moreau, Abderrahim Halimi, Philippe Ciuciu. Sparsity-based blind deconvolution of neural activation signal in fMRI. 2019 IEEE International Conference on Acoustic Speech and Signal Processing, May 2019, Brighton, United Kingdom. ⟨hal-02085810v2⟩

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