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arXiv · 1305.2788

HRF estimation improves sensitivity of fMRI encoding and decoding models

Fabian Pedregosa·Michael Eickenberg·Bertrand Thirion·Alexandre Gramfort
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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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Fabian Pedregosa, Michael Eickenberg, Bertrand Thirion, Alexandre Gramfort. 2013-05-13. HRF estimation improves sensitivity of fMRI encoding and decoding models. https://arxiv.org/abs/1305.2788

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