title: Data-driven HRF estimation for encoding and decoding models.
abstract: Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large. We develop a method for the joint estimation of activation and HRF by means of a rank constraint, forcing the estimated HRF to be equal across events or experimental conditions, yet permitting it to differ across voxels. Model estimation leads to an optimization problem that we propose to solve with an efficient quasi-Newton method, exploiting fast gradient computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be extended to the setting of GLM with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments. We compare 10 different HRF modeling methods in terms of encoding and decoding scores on two different datasets. Our results show that the R1-GLM model outperforms competing methods in both encoding and decoding settings, positioning it as an attractive method both from the points of view of accuracy and computational efficiency.
authors.affiliation: Parietal Team, INRIA Saclay-Île-de-France, Saclay, France; NeuroSpin, CEA Saclay, Bat. 145, 91191 Gif-sur-Yvette Cedex, France. Electronic address: fabian.pedregosa@inria.fr.
authors.forename: Fabian
authors.initials: F
authors.lastname: Pedregosa
authors.affiliation: Parietal Team, INRIA Saclay-Île-de-France, Saclay, France; NeuroSpin, CEA Saclay, Bat. 145, 91191 Gif-sur-Yvette Cedex, France.
authors.forename: Michael
authors.initials: M
authors.lastname: Eickenberg
authors.affiliation: Parietal Team, INRIA Saclay-Île-de-France, Saclay, France; NeuroSpin, CEA Saclay, Bat. 145, 91191 Gif-sur-Yvette Cedex, France.
authors.forename: Philippe
authors.initials: P
authors.lastname: Ciuciu
authors.affiliation: Parietal Team, INRIA Saclay-Île-de-France, Saclay, France; NeuroSpin, CEA Saclay, Bat. 145, 91191 Gif-sur-Yvette Cedex, France.
authors.forename: Bertrand
authors.initials: B
authors.lastname: Thirion
authors.affiliation: Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI, 37-39 Rue Dareau, 75014 Paris, France; NeuroSpin, CEA Saclay, Bat. 145, 91191 Gif-sur-Yvette Cedex, France.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Gramfort
mesh_terms: D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D000069336:Neurovascular Coupling; D012044:Regression Analysis; D014796:Visual Perception / Q000502:physiology
keywords: BOLD; Decoding; Encoding; Finite impulse response (FIR); Functional MRI (fMRI); Hemodynamic response function (HRF); Machine learning; Optimization