fMRI BOLD Signal Decomposition Using a Multivariate Low-Rank Model

Hamza Cherkaoui, Thomas Moreau, Abderrahim Halimi, Philippe Ciuciu

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

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Abstract

Standard methodologies for functional Magnetic Resonance Imaging (fMRI) data analysis decompose the observed Blood Oxygenation Level Dependent (BOLD) signals using voxel-wise linear model and perform maximum likelihood estimation to get the parameters associated with the regressors. In task fMRI, the latter are usually defined from the experimental paradigm and some confounds whereas in resting-state acquisitions, a seed-voxel time-course may be used as predictor. Nowadays, most fMRI datasets offer resting-state acquisitions, requiring multivariate approaches (e.g., PCA, ICA, etc) to extract meaningful information in a data-driven manner. Here, we propose a novel low-rank model of fMRI BOLD data but instead of considering a dimension reduction in space as in ICA, our model relies on convolutional sparse coding between the hemodynamic system and a few temporal atoms which code for the neural activity inducing signals. A rank-1 constraint is also associated with each temporal atom to spatially map its influence in the brain. Within a variational framework, the joint estimation of the neural signals and the associated spatial maps is formulated as a non-convex optimization problem. A local minimizer is computed using an efficient alternate minimization algorithm. The proposed approach is first validated on simulations and then applied to task fMRI data for illustration purpose. Its comparison to a state-of-the-art approach suggests that our method is competitive regarding the uncovered neural fingerprints while offering a richer decomposition in time and space.

Original languageEnglish
Title of host publication2019 27th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
ISBN (Electronic)9789082797039
DOIs
Publication statusPublished - 18 Nov 2019
Event27th European Signal Processing Conference 2019 - A Coruna, Spain, A Coruna, Spain
Duration: 2 Sep 20197 Sep 2019
http://eusipco2019.org/

Publication series

NameEuropean Signal Processing Conference
ISSN (Electronic)2076-1465

Conference

Conference27th European Signal Processing Conference 2019
Abbreviated titleEUSIPCO
CountrySpain
CityA Coruna
Period2/09/197/09/19
Internet address

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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    Cherkaoui, H., Moreau, T., Halimi, A., & Ciuciu, P. (2019). fMRI BOLD Signal Decomposition Using a Multivariate Low-Rank Model. In 2019 27th European Signal Processing Conference (EUSIPCO) [8902660] (European Signal Processing Conference). IEEE. https://doi.org/10.23919/EUSIPCO.2019.8902660