HRF estimation improves sensitivity of fMRI encoding and decoding models

TL;DR

This paper introduces a joint estimation method for the HRF and activation patterns in fMRI data, improving the sensitivity of encoding and decoding models by accounting for HRF variability across subjects and regions.

Contribution

It presents a novel low-rank model for simultaneous HRF and activation estimation, enhancing analysis accuracy in rapid-event fMRI designs.

Findings

Improved encoding and decoding performance using the estimated activation patterns.

Demonstrated robustness of the model across different subjects and brain regions.

Enhanced sensitivity in detecting neural activation patterns.

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.