Modeling the hemodynamic response in single‐trial functional MRI experiments

Today, most studies of cognitive processes using functional MRI (fMRI) experiments adopt a single‐trial design. Highly flexible stimulation paradigms require new statistical models in which not only the activation amount but also the time course of the measured hemodynamic response is analyzed. Most previous approaches have been based on a linear regression context and have introduced hemodynamic model functions to improve the signal detection. In this report a nonlinear regression context is derived, from which shape parameters for the hemodynamic response are obtained per trial and per region of interest. These parameters allow the investigation of stimulus‐induced shape variations of the hemodynamic response. By embedding the estimation into a robust statistical framework and rigorously analyzing the spatiotemporal interactions in the fMRI data, it is possible to derive statistically valid descriptions of single hemodynamic responses. The model, estimation algorithm, validation, and an example analysis from a single‐trial fMRI study are reported. Magn Reson Med 42:787–797, 1999. © 1999 Wiley‐Liss, Inc.

[1]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[2]  Azriel Rosenfeld,et al.  Digital Picture Processing , 1976 .

[3]  K. Vahala Handbook of stochastic methods for physics, chemistry and the natural sciences , 1986, IEEE Journal of Quantum Electronics.

[4]  K. Borgwardt The Simplex Method: A Probabilistic Analysis , 1986 .

[5]  R. Kass Nonlinear Regression Analysis and its Applications , 1990 .

[6]  B. Rosen,et al.  Functional mapping of the human visual cortex by magnetic resonance imaging. , 1991, Science.

[7]  R. Turner,et al.  Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Ravi S. Menon,et al.  Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[10]  D. Tank,et al.  4 Tesla gradient recalled echo characteristics of photic stimulation‐induced signal changes in the human primary visual cortex , 1993 .

[11]  Karl J. Friston,et al.  Assessing the significance of focal activations using their spatial extent , 1994, Human brain mapping.

[12]  J. Hennig,et al.  Observation of a fast response in functional MR , 1994, Magnetic resonance in medicine.

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

[14]  A Villringer,et al.  Coupling of brain activity and cerebral blood flow: basis of functional neuroimaging. , 1995, Cerebrovascular and brain metabolism reviews.

[15]  C. Cox,et al.  Asymptotic confidence bands for generalized nonlinear regression models. , 1995, Biometrics.

[16]  E. Bullmore,et al.  Statistical methods of estimation and inference for functional MR image analysis , 1996, Magnetic resonance in medicine.

[17]  A. Georgopoulos,et al.  Time‐resolved fMRI of mental rotation , 1997, Neuroreport.

[18]  Karl J. Friston,et al.  Event-related fMRI , 1997 .

[19]  Eric Walter,et al.  Identification of Parametric Models: from Experimental Data , 1997 .

[20]  M. D’Esposito,et al.  A Trial-Based Experimental Design for fMRI , 1997, NeuroImage.

[21]  A. Neumaier,et al.  Multivariate Autoregressive and Ornstein-Uhlenbeck Processes: Estimates for Order, Parameters, Spect , 1997 .

[22]  Mark S. Cohen,et al.  Parametric Analysis of fMRI Data Using Linear Systems Methods , 1997, NeuroImage.

[23]  Irène Buvat,et al.  Space-Time Statistical Model for Functional MRI Image Sequences , 1997, IPMI.

[24]  Scott L. Zeger,et al.  Non‐linear Fourier Time Series Analysis for Human Brain Mapping by Functional Magnetic Resonance Imaging , 1997 .

[25]  R. Menon,et al.  Millisecond sequencing of neural activation in simple tasks determined by the BOLD fMRI neurovascular response , 1998, NeuroImage.

[26]  Xavier Descombes,et al.  fMRI Signal Restoration Using a Spatio-Temporal Markov Random Field Preserving Transitions , 1998, NeuroImage.

[27]  Karl J. Friston,et al.  Nonlinear event‐related responses in fMRI , 1998, Magnetic resonance in medicine.

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

[29]  A. Georgopoulos,et al.  Time-Resolved fMRI of Motor Area Activity During Mental Rotation , 1998, NeuroImage.

[30]  J. R. Koehler,et al.  Modern Applied Statistics with S-Plus. , 1996 .