title: Particle filtering for nonlinear BOLD signal analysis.
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.
authors.affiliation: Howard Florey Institute & Centre for Neuroscience, Melbourne, Australia. l.johnston@hfi.unimelb.edu.au
authors.forename: Leigh A
authors.initials: LA
authors.lastname: Johnston
authors.forename: Eugene
authors.initials: E
authors.lastname: Duff
authors.forename: Gary F
authors.initials: GF
authors.lastname: Egan
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000098:blood supply* / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D010100:Oxygen / Q000378:metabolism*; D010101:Oxygen Consumption / Q000502:physiology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D010100:Oxygen