Statistical methods of estimation and inference for functional MR image analysis

Statistical methods of estimation and inference for functional MR image analysis Two questions arising In the analysis of functional magnetic resonance imaging (fMRI) data acquired during periodic sensory stimulation are: i) how to measure the experimentally determined effect in fMRI time series; and ii) how to decide whether an apparent effect is significant Our approach is first to fit a time series regression model, including sine and cosine terms at the (fundamental) frequency of experimental stimulation, by pseudogeneralized least squares (PGLS) at each pixel of an image. Sinusoidal modeling takes account of locally variable hemodynamic delay and dispersion, and PGLS fitting corrects for residual or endogenous autocorrelation in fMRI time series, to yield best unbiased estimates of the amplitudes of the sine and cosine terms at fundamental frequency; from these parameters the authors derive estimates of experimentally determined power and its standard error. Randomization testing is then used to create inferential brain activation maps (BAMs) of pixels significantly activated by the experimental stimulus. The methods are illustrated by application to data acquired from normal human subjects during periodic visual and auditory stimulation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Magnetic Resonance in Medicine Wiley

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Publisher
Wiley
Copyright
Copyright © 1996 Wiley‐Liss, Inc., A Wiley Company
ISSN
0740-3194
eISSN
1522-2594
DOI
10.1002/mrm.1910350219
Publisher site
See Article on Publisher Site

Abstract

Two questions arising In the analysis of functional magnetic resonance imaging (fMRI) data acquired during periodic sensory stimulation are: i) how to measure the experimentally determined effect in fMRI time series; and ii) how to decide whether an apparent effect is significant Our approach is first to fit a time series regression model, including sine and cosine terms at the (fundamental) frequency of experimental stimulation, by pseudogeneralized least squares (PGLS) at each pixel of an image. Sinusoidal modeling takes account of locally variable hemodynamic delay and dispersion, and PGLS fitting corrects for residual or endogenous autocorrelation in fMRI time series, to yield best unbiased estimates of the amplitudes of the sine and cosine terms at fundamental frequency; from these parameters the authors derive estimates of experimentally determined power and its standard error. Randomization testing is then used to create inferential brain activation maps (BAMs) of pixels significantly activated by the experimental stimulus. The methods are illustrated by application to data acquired from normal human subjects during periodic visual and auditory stimulation.

Journal

Magnetic Resonance in MedicineWiley

Published: Feb 1, 1996

References

  • The analysis of functional MRI time series
    Friston, Friston; Jezzard, Jezzard; Turner, Turner
  • Functional and effective connectivity in neuroimaging: a synthesis
    Friston, Friston

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