HRF Estimation Improves Sensitivity of fMRI Encoding and Decoding Models

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 fix...

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Bibliographic Details
Published in2013 International Workshop on Pattern Recognition in Neuroimaging pp. 165 - 169
Main Authors Pedregosa, Fabian, Eickenberg, Michael, Thirion, Bertrand, Gramfort, Alexandre
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
BOLD
Brain models
Computational modeling
Decoding
Encoding
Estimation
fMRI
GLM
hemodynamic
HRF
Vectors
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Summary: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.
DOI:10.1109/PRNI.2013.50