Extracting activation patterns from functional Magnetic Resonance Images (fMRI) datasets remains challenging in rapid-event designs due to the inherent delay of blood oxygen level-dependent (BOLD) signal. The general linear model (GLM) allows to estimate the activation from a design matrix and a fixed hemodynamic response function (HRF). However, the HRF is known to vary substantially between subjects and brain regions. In this paper, we propose a model for jointly estimating the hemodynamic response function (HRF) and the activation patterns via a low-rank representation of task effects.This model is based on the linearity assumption behind the GLM and can be computed using standard gradient-based solvers. We use the activation patterns computed by our model as input data for encoding and decoding studies and report performance improvement in both settings.
Cite
@article{arxiv.1305.2788,
title = {HRF estimation improves sensitivity of fMRI encoding and decoding models},
author = {Fabian Pedregosa and Michael Eickenberg and Bertrand Thirion and Alexandre Gramfort},
journal= {arXiv preprint arXiv:1305.2788},
year = {2013}
}
Comments
3nd International Workshop on Pattern Recognition in NeuroImaging (2013)