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HRF estimation improves sensitivity of fMRI encoding and decoding models
Abstract : 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.
Cited literature [10 references]
https://hal.inria.fr/hal-00821946
Contributor : Fabian Pedregosa Connect in order to contact the contributor
Submitted on : Monday, May 13, 2013 - 3:57:47 PM
Last modification on : Friday, January 21, 2022 - 3:15:10 AM
Long-term archiving on: : Tuesday, April 4, 2017 - 6:24:16 AM
Contributor : Fabian Pedregosa Connect in order to contact the contributor
Submitted on : Monday, May 13, 2013 - 3:57:47 PM
Last modification on : Friday, January 21, 2022 - 3:15:10 AM
Long-term archiving on: : Tuesday, April 4, 2017 - 6:24:16 AM
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