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    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.

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Estimating the population local wavelet spectrum with application to non-stationary functional magnetic resonance imaging time series

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Estimating the population local wavelet spectrum with application to non-stationary functional magnetic resonance imaging time series. / Gott, Aimee; Eckley, Idris; Aston, John.

In: Statistics in Medicine, Vol. 34, No. 29, 20.12.2015, p. 3901-3915.

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@article{11faad26def948adbe7ed245bca5d630,
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.",
keywords = "locally stationary, replicate, random effects, wavelet processes, fMRI",
author = "Aimee Gott and Idris Eckley and John Aston",
note = "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.",
year = "2015",
month = dec,
day = "20",
doi = "10.1002/sim.6592",
language = "English",
volume = "34",
pages = "3901--3915",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "29",

}

RIS

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 -