Rights statement: This is the peer reviewed version of the following article: Gott, A. N., Eckley, I. A., and Aston, J. A. D. (2015) Estimating the population local wavelet spectrum with application to non-stationary functional magnetic resonance imaging time series. Statist. Med., doi: 10.1002/sim.6592 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/sim.6592/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Accepted author manuscript, 478 KB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Estimating the population local wavelet spectrum with application to non-stationary functional magnetic resonance imaging time series
AU - Gott, Aimee
AU - Eckley, Idris
AU - Aston, John
N1 - This is the peer reviewed version of the following article: Gott, A. N., Eckley, I. A., and Aston, J. A. D. (2015) Estimating the population local wavelet spectrum with application to non-stationary functional magnetic resonance imaging time series. Statist. Med., doi: 10.1002/sim.6592 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/sim.6592/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
PY - 2015/12/20
Y1 - 2015/12/20
N2 - 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.
AB - 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.
KW - locally stationary
KW - replicate
KW - random effects
KW - wavelet processes
KW - fMRI
U2 - 10.1002/sim.6592
DO - 10.1002/sim.6592
M3 - Journal article
VL - 34
SP - 3901
EP - 3915
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 29
ER -