title: Estimating the population local wavelet spectrum with application to non-stationary functional magnetic resonance imaging time series.
abstract: Functional magnetic resonance imaging (fMRI) is a dynamic four-dimensional imaging modality. However, in almost all fMRI analyses, the time series elements of this data are assumed to be second-order stationary. In this paper, we examine, using time series spectral methods, whether such stationary assumptions can be made and whether estimates of non-stationarity can be used to gain understanding into fMRI experiments. A non-stationary version of replicated stationary time series analysis is proposed that takes into account the replicated time series that are available from nearby voxels in a region of interest (ROI). These are used to investigate non-stationarities in both the ROI itself and the variations within the ROI. The proposed techniques are applied to simulated data and to an anxiety-inducing fMRI experiment.
authors: Department of Mathematics and Statistics, Lancaster University, Lancaster, U.K.
authors: Aimee N
authors: AN
authors: Gott
authors: Department of Mathematics and Statistics, Lancaster University, Lancaster, U.K.
authors: Idris A
authors: IA
authors: Eckley
authors: Statistical Laboratory, University of Cambridge, Cambridge, U.K.
authors: John A D
authors: JA
authors: Aston
mesh_terms: D001007:Anxiety / Q000503:physiopathology*; D015982:Bias; D001921:Brain / Q000098:blood supply / Q000502:physiology*; D001923:Brain Chemistry / Q000502:physiology; D003198:Computer Simulation; D059907:Functional Neuroimaging / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010100:Oxygen / Q000097:blood; D012815:Signal Processing, Computer-Assisted; D013057:Spectrum Analysis / Q000379:methods*; D013997:Time Factors; D058067:Wavelet Analysis
chemical_list: D010100:Oxygen
keywords: fMRI; locally stationary; random effects; replicate; wavelet processes