A state-space model of the hemodynamic approach: nonlinear filtering of BOLD signals
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Naoki Miura | Tohru Ozaki | Jobu Watanabe | Kazuki Iwata | Jorge J. Riera | Ryuta Kawashima | Eduardo Aubert | T. Ozaki | R. Kawashima | J. Riera | N. Miura | J. Watanabe | E. Aubert | K. Iwata
[1] Scott A. Huettel,et al. Regional Differences in the Refractory Period of the Hemodynamic Response: An Event-Related fMRI Study , 2001, NeuroImage.
[2] J. R. Baker,et al. The intravascular contribution to fmri signal change: monte carlo modeling and diffusion‐weighted studies in vivo , 1995, Magnetic resonance in medicine.
[3] T. Sejnowski,et al. Single-Trial Variability in Event-Related BOLD Signals , 2002, NeuroImage.
[4] M. Raichle,et al. The Effects of Changes in PaCO2 Cerebral Blood Volume, Blood Flow, and Vascular Mean Transit Time , 1974, Stroke.
[5] Tohru Ozaki. Identification of nonlinearities and non-Gaussianities in time series , 1992 .
[6] Juan C. Jiménez,et al. Linear estimation of continuous-discrete linear state space models with multiplicative noise , 2002, Syst. Control. Lett..
[7] B. Rosen,et al. Evidence of a Cerebrovascular Postarteriole Windkessel with Delayed Compliance , 1999, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.
[8] Robert Costalat,et al. A Model of the Coupling between Brain Electrical Activity, Metabolism, and Hemodynamics: Application to the Interpretation of Functional Neuroimaging , 2002, NeuroImage.
[9] Tohru Ozaki,et al. An Approximate Innovation Method For The Estimation Of Diffusion Processes From Discrete Data , 2006 .
[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] C. Loan. Computing integrals involving the matrix exponential , 1978 .
[12] T. Ozaki. 2 Non-linear time series models and dynamical systems , 1985 .
[13] Eric R. Ziecel. Proceedings of the First U.S./Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach , 1994 .
[14] R. Buxton,et al. Dynamics of blood flow and oxygenation changes during brain activation: The balloon model , 1998, Magnetic resonance in medicine.
[15] J. C. Jimenez,et al. A numerical method for the computation of the Lyapunov exponents of nonlinear ordinary differential equations , 2002, Appl. Math. Comput..
[16] Karl J. Friston,et al. Bayesian Estimation of Dynamical Systems: An Application to fMRI , 2002, NeuroImage.
[17] P. Bandettini,et al. Spatial Heterogeneity of the Nonlinear Dynamics in the FMRI BOLD Response , 2001, NeuroImage.
[18] R. C. Oldfield. The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.
[19] M. D’Esposito,et al. The Variability of Human, BOLD Hemodynamic Responses , 1998, NeuroImage.
[20] Ravi S. Menon,et al. Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. , 1993, Biophysical journal.
[21] 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.
[22] G. McCarthy,et al. Evidence for a Refractory Period in the Hemodynamic Response to Visual Stimuli as Measured by MRI , 2000, NeuroImage.
[23] Juan Carlos Jimenez,et al. A simple algebraic expression to evaluate the local linearization schemes for stochastic differential equations , 2002, Appl. Math. Lett..
[24] R. J. Biscay,et al. Approximation of continuous time stochastic processes by the local linearization method revisited , 2002 .
[25] J. C. Jimenez,et al. Local Linearization method for the numerical solution of stochastic differential equations , 1996 .
[26] P. Magistretti,et al. Cellular mechanisms of brain energy metabolism and their relevance to functional brain imaging. , 1999, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[27] Karl J. Friston,et al. Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics , 2000, NeuroImage.
[28] Tohru Ozaki. The Local Linearization Filter with Application to Nonlinear System Identifications , 1994 .
[29] C. Iadecola,et al. Intrinsic signals and functional brain mapping: caution, blood vessels at work. , 2002, Cerebral cortex.
[30] J. C. Jimenez,et al. Simulation of Stochastic Differential Equations Through the Local Linearization Method. A Comparative Study , 1999 .
[31] Juan C. Jiménez,et al. Nonlinear EEG analysis based on a neural mass model , 1999, Biological Cybernetics.
[32] J. C. Jimenez,et al. Local linearization filters for non-linear continuous-discrete state space models with multiplicative noise , 2003 .
[33] Rolando J. Biscay,et al. Computing the noise covariance matrix of the local linearization scheme for the numerical solution of stochastic differential equations , 1998 .
[34] Lars Kai Hansen,et al. Modeling the hemodynamic response in fMRI using smooth FIR filters , 2000, IEEE Transactions on Medical Imaging.
[35] Tohru Ozaki,et al. The Role of the Likelihood Function in the Estimation of Chaos Models , 2000 .
[36] Juan C. Jiménez,et al. Dynamic properties of the local linearization method for initial-value problems , 2002, Appl. Math. Comput..
[37] R. Cox,et al. Event‐related fMRI contrast when using constant interstimulus interval: Theory and experiment , 2000, Magnetic resonance in medicine.
[38] H. Benali,et al. Robust Bayesian estimation of the hemodynamic response function in event‐related BOLD fMRI using basic physiological information , 2003, Human brain mapping.
[39] Ying Zheng,et al. A Model of the Hemodynamic Response and Oxygen Delivery to Brain , 2002, NeuroImage.
[40] J. Rajapakse,et al. Human Brain Mapping 6:283–300(1998) � Modeling Hemodynamic Response for Analysis of Functional MRI Time-Series , 2022 .
[41] T. Ozaki. A local linearization approach to nonlinear filtering , 1993 .
[42] Karl J. Friston,et al. Nonlinear Coupling between Evoked rCBF and BOLD Signals: A Simulation Study of Hemodynamic Responses , 2001, NeuroImage.
[43] Karl J. Friston,et al. Analysis of functional MRI time‐series , 1994, Human Brain Mapping.
[44] J. Runnenburg. PROBABILITY THEORY AND ITS APPLICATIONS , 1985 .
[45] Scott L. Zeger,et al. Non‐linear Fourier Time Series Analysis for Human Brain Mapping by Functional Magnetic Resonance Imaging , 1997 .
[46] Héctor de Arazoza,et al. Nonlinear Parametric Model Identification Using Genetic Algorithms , 2003, IWANN.
[47] Angela R Laird,et al. Characterizing instantaneous phase relationships in whole‐brain fMRI activation data , 2002, Human brain mapping.
[48] Karl J. Friston,et al. Nonlinear event‐related responses in fMRI , 1998, Magnetic resonance in medicine.
[49] G. Berns,et al. Continuous Functional Magnetic Resonance Imaging Reveals Dynamic Nonlinearities of “Dose-Response” Curves for Finger Opposition , 1999, The Journal of Neuroscience.