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Estimating the population local wavelet spectrum with application to non‐stationary functional magnetic resonance imaging time series
Author(s) -
Gott Aimee N.,
Eckley Idris A.,
Aston John A. D.
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6592
Subject(s) - series (stratigraphy) , wavelet , magnetic resonance imaging , spectrum (functional analysis) , functional magnetic resonance imaging , population , time series , statistical physics , computer science , mathematics , statistics , physics , medicine , artificial intelligence , radiology , geology , paleontology , environmental health , quantum mechanics
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. Copyright © 2015 John Wiley & Sons, Ltd.