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Particle Filtering for Nonlinear BOLD Signal Analysis

  • Leigh A. Johnston
  • Eugene Duff
  • Gary F. Egan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

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.

Keywords

Particle Filter Cerebral Blood Volume Primary Motor Cortex Volterra Kernel Balloon Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Leigh A. Johnston
    • 1
    • 2
  • Eugene Duff
    • 1
    • 3
  • Gary F. Egan
    • 1
  1. 1.Howard Florey Institute & Centre for NeuroscienceMelbourneAustralia
  2. 2.Dept. of Electrical & Electronic EngineeringUniversity of MelbourneAustralia
  3. 3.Dept. of Mathematics & StatisticsUniversity of MelbourneAustralia

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