HRF estimation improves sensitivity of fMRI encoding and decoding models
This work addresses variability in HRF for fMRI analysis, offering incremental improvements for neuroscience researchers using encoding and decoding models.
The paper tackled the challenge of extracting activation patterns from fMRI data in rapid-event designs by jointly estimating the hemodynamic response function (HRF) and activation patterns using a low-rank representation, resulting in performance improvements in encoding and decoding studies.
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.