Sparsity-based blind deconvolution of neural activation signal in FMRI

Hamza Cherkaoui, Thomas Moreau, Abderrahim Halimi, Philippe Ciuciu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
16 Downloads (Pure)

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.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages1323-1327
Number of pages5
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - 17 Apr 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Publication series

NameIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISSN (Electronic)2379-190X

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing 2019
Abbreviated titleICASSP 2019
CountryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Keywords

  • BOLD signal
  • Hemodynamic response function (HRF)
  • non-convex optimization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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