Robust Bayesian estimation of the hemodynamic response function in event‐related BOLD fMRI using basic physiological information

In BOLD fMRI data analysis, robust and accurate estimation of the Hemodynamic Response Function (HRF) is still under investigation. Parametric methods assume the shape of the HRF to be known and constant throughout the brain, whereas non‐parametric methods mostly rely on artificially increasing the signal‐to‐noise ratio. We extend and develop a previously proposed method that makes use of basic yet relevant temporal information about the underlying physiological process of the brain BOLD response in order to infer the HRF in a Bayesian framework. A general hypothesis test is also proposed, allowing to take advantage of the knowledge gained regarding the HRF to perform activation detection. The performances of the method are then evaluated by simulation. Great improvement is shown compared to the Maximum‐Likelihood estimate in terms of estimation error, variance, and bias. Robustness of the estimators with regard to the actual noise structure or level, as well as the stimulus sequence, is also proven. Lastly, fMRI data with an event‐related paradigm are analyzed. As suspected, the regions selected from highly discriminating activation maps resulting from the method exhibit a certain inter‐regional homogeneity in term of HRF shape, as well as noticeable inter‐regional differences. Hum. Brain Mapping 19:1–17, 2003. © 2003 Wiley‐Liss, Inc.

[1]  R. Kass,et al.  Approximate Bayesian Inference in Conditionally Independent Hierarchical Models (Parametric Empirical Bayes Models) , 1989 .

[2]  G. L. Bretthorst,et al.  Bayesian Interpolation and Deconvolution , 1992 .

[3]  G. L. Bretthorst,et al.  Bayesian analysis. V : Amplitude estimation for multiple well-separated sinusoids , 1992 .

[4]  Karl J. Friston,et al.  Analysis of functional MRI time‐series , 1994, Human Brain Mapping.

[5]  Karl J. Friston,et al.  Analysis of fMRI Time-Series Revisited—Again , 1995, NeuroImage.

[6]  K. Riedel Numerical Bayesian Methods Applied to Signal Processing , 1996 .

[7]  R. Buxton,et al.  A Model for the Coupling between Cerebral Blood Flow and Oxygen Metabolism during Neural Stimulation , 1997, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[8]  A. Dale,et al.  Selective averaging of rapidly presented individual trials using fMRI , 1997, Human brain mapping.

[9]  M. D’Esposito,et al.  The variability of human BOLD hemodynamic responses , 1998, NeuroImage.

[10]  A. Dale,et al.  Functional–Anatomic Study of Episodic Retrieval II. Selective Averaging of Event-Related fMRI Trials to Test the Retrieval Success Hypothesis , 1998, NeuroImage.

[11]  D. Schacter,et al.  Functional–Anatomic Study of Episodic Retrieval Using fMRI I. Retrieval Effort versus Retrieval Success , 1998, NeuroImage.

[12]  R. Buxton,et al.  Dynamics of blood flow and oxygenation changes during brain activation: The balloon model , 1998, Magnetic resonance in medicine.

[13]  J. Rajapakse,et al.  Human Brain Mapping 6:283–300(1998) � Modeling Hemodynamic Response for Analysis of Functional MRI Time-Series , 2022 .

[14]  M. A. Tanner,et al.  Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, 3rd Edition , 1998 .

[15]  R. Buckner,et al.  Human Brain Mapping 6:373–377(1998) � Event-Related fMRI and the Hemodynamic Response , 2022 .

[16]  F Kruggel,et al.  Modeling the hemodynamic response in single‐trial functional MRI experiments , 1999, Magnetic resonance in medicine.

[17]  G. L. Bretthorst The Near-Irrelevance of Sampling Frequency Distributions , 1999 .

[18]  Richard F. Gunst,et al.  Applied Regression Analysis , 1999, Technometrics.

[19]  A M Dale,et al.  Optimal experimental design for event‐related fMRI , 1999, Human brain mapping.

[20]  D. V. von Cramon,et al.  Temporal properties of the hemodynamic response in functional MRI , 1999, Human brain mapping.

[21]  Iwao Kanno,et al.  A Bayesian approach to estimating the haemodynamic response function in event-related fMRI , 2000, NeuroImage.

[22]  Anthony G. Hudetz,et al.  Decoupling of the hemodynamic delay from the task-induced delay in FMRI , 2000, NeuroImage.

[23]  A M Dale,et al.  Estimation and detection of event‐related fMRI signals with temporally correlated noise: A statistically efficient and unbiased approach , 2000, Human brain mapping.

[24]  Lars Kai Hansen,et al.  Modeling the hemodynamic response in fMRI using smooth FIR filters , 2000, IEEE Transactions on Medical Imaging.

[25]  R. Buxton,et al.  Sorting out event-related paradigms in fMRI: the distinction between detecting an activation and estimating the hemodynamic response , 2000, NeuroImage.

[26]  Gary H. Glover,et al.  Assessment of Hemodynamic Response during Focal Neural Activity in Human Using Bolus Tracking, Arterial Spin Labeling and BOLD Techniques , 2000, NeuroImage.

[27]  D. V. Cramon,et al.  Nonlinear Regression of Functional MRI Data: An Item Recognition Task Study , 2000, NeuroImage.

[28]  S. Petersen,et al.  Characterizing the Hemodynamic Response: Effects of Presentation Rate, Sampling Procedure, and the Possibility of Ordering Brain Activity Based on Relative Timing , 2000, NeuroImage.

[29]  L. Fahrmeir,et al.  Bayesian Modeling of the Hemodynamic Response Function in BOLD fMRI , 2001, NeuroImage.

[30]  Habib Benali,et al.  Non-parametric Bayesian deconvolution of fMRI hemodynamic response function using smoothing prior , 2001, NeuroImage.

[31]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[32]  C Gössl,et al.  Bayesian Spatiotemporal Inference in Functional Magnetic Resonance Imaging , 2001, Biometrics.

[33]  Karl J. Friston,et al.  Classical and Bayesian Inference in Neuroimaging: Applications , 2002, NeuroImage.

[34]  Karl J. Friston,et al.  Bayesian Estimation of Dynamical Systems: An Application to fMRI , 2002, NeuroImage.

[35]  Jean-Baptiste Poline,et al.  Bayesian estimation of the hemodynamic response function in functional MRI , 2002 .

[36]  Karl J. Friston,et al.  Classical and Bayesian Inference in Neuroimaging: Theory , 2002, NeuroImage.