Section: New Results
fMRI encoding and decoding models
Participant : Fabian Pedregosa [correspondent] .
In  we show that HRF estimation improves sensitivity of fMRI encoding and decoding models and propose a new approach for the estimation of Hemodynamic Response Functions from fMRI data. The model we propose is based on the linearity assumption behind the General Linear Model 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.
This work proves that significant improvements in recovery of brain activation patterns can be made by estimating the form of the Hemodynamic Response Function instead of using a canonical form for this response.