Name
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. |
Year | Venue | Keywords |
|---|---|---|
2019 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | BOLD signal, Hemodynamic response function (HRF), non-convex optimization |
Field | DocType | ISSN |
Non convex optimization,Pattern recognition,Functional magnetic resonance imaging,Blind deconvolution,Computer science,Deconvolution,Neural activity,Artificial intelligence,Cognition,Minimization algorithm | Conference | 1520-6149 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hamza Cherkaoui | 1 | 0 | 1.35 |
| Thomas Moreau | 2 | 7 | 7.56 |
| Abderrahim Halimi | 3 | 292 | 20.72 |
| Philippe Ciuciu | 4 | 452 | 50.82 |