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HRF estimation improves sensitivity of fMRI encoding and decoding models

Fabian Pedregosa 1, 2 Michael Eickenberg 3, 2 Bertrand Thirion 2, 3 Alexandre Gramfort 4, 5
1 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
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.
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Contributor : Fabian Pedregosa <>
Submitted on : Monday, May 13, 2013 - 3:57:47 PM
Last modification on : Tuesday, September 22, 2020 - 3:49:38 AM
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  • HAL Id : hal-00821946, version 1
  • ARXIV : 1305.2788


Fabian Pedregosa, Michael Eickenberg, Bertrand Thirion, Alexandre Gramfort. HRF estimation improves sensitivity of fMRI encoding and decoding models. 3nd International Workshop on Pattern Recognition in NeuroImaging, Jun 2013, Philadelphia, United States. ⟨hal-00821946⟩



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