Abstract
Functional magnetic resonance (fMRI) data are often corrupted with colored noise. To account for this type of noise, many pre-whitening and pre-coloring strategies have been proposed to process the fMRI time series prior to statistical inference. In this paper, a generalized likelihood ratio test for brain activation detection is proposed in which the temporal correlation structure of the noise is modelled as an autoregressive (AR) model. The order of the AR model is determined from experimental null data sets. Simulation tests reveal that, for a fixed false alarm rate, the proposed test is slightly (2-3%) better than current tests incorporating colored noise in terms of detection rate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cohen, M.S.: Real-time functional magnetic resonance imaging. Methods 25, 201–220 (2001)
Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.B., Frith, C.D., Frackowiak, R.S.J.: Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2, 189–210 (1995)
Worsley, K.J., Liao, C.H., Aston, J., Petre, V., Duncan, G.H., Morales, F., Evans, A.C.: A general statistical analysis for fMRI data. NeuroImage 15, 1–15 (2002)
Woolrich, M.W., Ripley, B.D., Brady, J.M., Smith, S.M.: Temporal autocorrelation in univariate linear modelling of fMRI data. NeuroImage 14, 1370–1386 (2001)
Worsley, K.J., Friston, K.J.: Analysis of fMRI time-series revisited – again. NeuroImage 2, 173–181 (1995)
Kay, S.M., Marple, S.L.: Spectrum analysis – a modern perspective. Proceedings of the IEEE 69, 1380–1419 (1981)
Carew, J.D., Wahba, G., Xie, X., Nordheim, E.V., Meyerand, M.E.: Optimal spline smoothing of fMRI time series by generalized cross-validation. NeuroImage 18, 950–961 (2003)
den Dekker, A.J., Sijbers, J.: Estimation of signal and noise from MR data. In: Advanced Image Processing in Magnetic Resonance Imaging of Signal Processing and Communications, vol. 26, Marcel Dekker, New York (2005) ISBN: 0824725425
van den Bos, A.: 8: Parameter Estimation. In: Sydenham, P.H. (ed.) Handbook of Measurement Science, vol. 1, pp. 331–377. Wiley, Chichester (1982)
Priestley, M.B.: Spectral analysis and time series. Academic Press, London (1981)
Kay, S.M.: Fundamentals of statistical signal processing: estimation theory. Prentice-Hall, Inc., Englewood Cliffs (1993)
Rowe, D.B., Logan, B.R.: A complex way to compute fMRI activation. Neuroimage 23, 1078–1092 (2004)
Ardekani, B.A., Kershaw, J., Kashikura, K., Kanno, I.: Activation detection in functional MRI using subspace modeling and maximum likelihood estimation. IEEE Trans Med Imaging 18, 246–254 (1999)
Broersen, P.M.T.: Finite sample criteria for autoregressive order selection. IEEE Trans. Sig. Proc. 48, 3550–3558 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sijbers, J., den Dekker, A.J., Bos, R. (2005). A Likelihood Ratio Test for Functional MRI Data Analysis to Account for Colored Noise. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_68
Download citation
DOI: https://doi.org/10.1007/11558484_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29032-2
Online ISBN: 978-3-540-32046-3
eBook Packages: Computer ScienceComputer Science (R0)