title: Optimal HRF and smoothing parameters for fMRI time series within an autoregressive modeling framework.
abstract: The analysis of time series obtained by functional magnetic resonance imaging (fMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given fMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.
authors.affiliation: Department of Neuropediatrics, University of Kiel, 24098 Kiel, Germany. a.galka@neurologie.uni-kiel.de
authors.forename: Andreas
authors.initials: A
authors.lastname: Galka
authors.forename: Michael
authors.initials: M
authors.lastname: Siniatchkin
authors.forename: Ulrich
authors.initials: U
authors.lastname: Stephani
authors.forename: Kristina
authors.initials: K
authors.lastname: Groening
authors.forename: Stephan
authors.initials: S
authors.lastname: Wolff
authors.forename: Jorge
authors.initials: J
authors.lastname: Bosch-Bayard
authors.forename: Tohru
authors.initials: T
authors.lastname: Ozaki
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D002560:Cerebrovascular Circulation / Q000502:physiology; D003198:Computer Simulation / Q000592:standards*; D006439:Hemodynamics / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D012044:Regression Analysis; D013997:Time Factors