Abstract
Functional Magnetic Resonance imaging studies analyse sequences of brain volumes whose intensity changes predominantly reflect blood oxygenation level dependent (BOLD) effects. The most comprehensive signal model to date of the BOLD effect is formulated as a continuous-time system of nonlinear stochastic differential equations. In this paper we present a particle filtering method for the analysis of the BOLD system, and demonstrate it to be both accurate and robust in estimating the hidden physiological states including cerebral blood flow, cerebral blood volume, total deoxyhemoglobin content, and the flow inducing signal, from functional imaging data.
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Buxton, R.B., Uludağ, K., Dubowitz, D.J., Liu, T.T.: Modeling the hemodynamic response to brain activation. NeuroImage 23, S220–S233 (2004)
Riera, J.J., Watanabe, J., Kazuki, I., Naoki, M., Aubert, E., Ozaki, T., Kawashima, R.: A state-space model of the hemodynamic approach: nonlinear filtering of BOLD signals. NeuroImage 21, 547–567 (2004)
Buxton, R.B., Wong, E.C., Frank, L.R.: Dynamics of blood flow and oxygenation changes during brain activation: the Balloon model. Magnetic Resonance in Medicine 39, 855–864 (1998)
Mandeville, J.B., Marota, J.J., Ayata, C., Zararchuk, G., Moskowitz, M.A., Rosen, B., Weisskoff, R.M.: Evidence of a cerebrovascular postarteriole windkessel with delayed compliance. Journal of Cerebral Blood Flow and Metabolism 19, 679–689 (1999)
Friston, K.J., Mechelli, A., Turner, R., Price, C.J.: Nonlinear responses in fMRI: The Balloon model, Volterra kernels, and other hemodynamics. NeuroImage 12, 466–477 (2000)
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174–188 (2002)
Gilks, W.R., Berzuini, C.: Following a moving target – Monte Carlo inference for dynamic Bayesian models. Journal of the Royal Statistical Society, Series B 63(1), 127–146 (2001)
Ma, B., Ellis, R.E.: Unified Point Selection and Surface-Based Registration Using a Particle Filter. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 75–82. Springer, Heidelberg (2005)
Gard, T.C.: Introduction to Stochastic Differential Equations. Marcel Dekker, New York (1988)
Anderson, B.D.O., Moore, J.B.: Optimal Filtering. Prentice-Hall, Englewood Cliffs (1979)
Luenberger, D.G.: Linear and Nonlinear Programming, 2nd edn. Addison-Wesley, Reading (1984)
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Johnston, L.A., Duff, E., Egan, G.F. (2006). Particle Filtering for Nonlinear BOLD Signal Analysis. In: Larsen, R., Nielsen, M., Sporring, J. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. MICCAI 2006. Lecture Notes in Computer Science, vol 4191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11866763_36
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DOI: https://doi.org/10.1007/11866763_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44727-6
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