Data-driven HRF estimation for encoding and decoding models

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 da...

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Veröffentlicht in:	NeuroImage (Orlando, Fla.) Jg. 104; S. 209 - 220
Hauptverfasser:	Pedregosa, Fabian, Eickenberg, Michael, Ciuciu, Philippe, Thirion, Bertrand, Gramfort, Alexandre
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.01.2015 Elsevier Limited Elsevier
Schlagworte:	BOLD Brain - physiology Brain Mapping - methods Computer Science Decoding Derivatives Economic models Encoding Estimates Finite impulse response (FIR) Functional MRI (fMRI) Hemodynamic response function (HRF) Humans Image Processing, Computer-Assisted Machine Learning Magnetic Resonance Imaging - methods Methods Models, Neurological Neurovascular Coupling Optimization Regression Analysis Studies Visual Perception - physiology Machine learning Hemodynamic response function (HRF) Finite impulse response (FIR) BOLD Encoding Functional MRI (fMRI) Decoding Optimization Finite Impulse Response (FIR) Hemodynamic Response Function (HRF) Machine Learning
ISSN:	1053-8119, 1095-9572, 1095-9572
Online-Zugang:	Volltext
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