Neuroscience & Neuroimaging Research Collection

Journal article

Temporal autocorrelation in univariate linear modeling of FMRI data.

Abstract:: In functional magnetic resonance imaging statistical analysis there are problems with accounting for temporal autocorrelations when assessing change within voxels. Techniques to date have utilized temporal filtering strategies to either shape these autocorrelations or remove them. Shaping, or "coloring," attempts to negate the effects of not accurately knowing the intrinsic autocorrelations by imposing known autocorrelation via temporal filtering. Removing the autocorrelation, or "prewhitening," gives the best linear unbiased estimator, assuming that the autocorrelation is accurately known. For single-event designs, the efficiency of the estimator is considerably higher for prewhitening compared with coloring. However, it has been suggested that sufficiently accurate estimates of the autocorrelation are currently not available to give prewhitening acceptable bias. To overcome this, we consider different ways to estimate the autocorrelation for use in prewhitening. After high-pass filtering is performed, a Tukey taper (set to smooth the spectral density more than would normally be used in spectral density estimation) performs best. Importantly, estimation is further improved by using nonlinear spatial filtering to smooth the estimated autocorrelation, but only within tissue type. Using this approach when prewhitening reduced bias to close to zero at probability levels as low as 1 x 10(-5).

Publication status:: Published

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APA Style

Woolrich, M., Ripley, B., Brady, M., & Smith, S. (2001). Temporal autocorrelation in univariate linear modeling of FMRI data. NeuroImage, 14(6), 1370–1386.

MLA Style

Woolrich, M., et al. “Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data.” NeuroImage, vol. 14, no. 6, 2001, pp. 1370–86.

Chicago Style

Woolrich, M., B. Ripley, M. Brady, and S. Smith. 2001. “Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data.” NeuroImage 14 (6): 1370–86.
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Publisher copy:: 10.1006/nimg.2001.0931

Authors

+ Woolrich, M More by this author

Institution:: University of Oxford
Division:: MSD
Department:: Psychiatry
Role:: Author

+ Ripley, B More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Statistics
Role:: Author

+ Brady, M More by this author

Role:: Author

+ Smith, S More by this author

Role:: Author

Journal:: NeuroImage More from this journal
Volume:: 14
Issue:: 6
Pages:: 1370-1386
Publication date:: 2001-12-01
DOI:: 10.1006/nimg.2001.0931
EISSN:: 1095-9572
ISSN:: 1053-8119

Language:: English
Keywords:: Image Processing, Computer-Assisted

Humans

Image Enhancement

Linear Models

Fourier Analysis

Calibration

Artifacts

Magnetic Resonance Imaging

Brain
Pubs id:: pubs:64894
UUID:: uuid:e26cdb91-02a9-4252-b2f6-cc57a8e25844
Local pid:: pubs:64894
Source identifiers:: 64894
Deposit date:: 2012-12-19
ARK identifier:: ark:/29072/ora_e26cdb9102a94252b2f6cc57a8e25844

Terms of use

Copyright date:: 2001

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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