Mumford, Jeanette A. and Nichols, Thomas E.. (2006) Modeling and inference of multisubject fMRI data. IEEE Engineering in Medicine and Biology Magazine, Vol.25 (No.2). pp. 42-51. ISSN 0739-5175

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Abstract

Functional magnetic resonance imaging (fMRI) is a rapidly growing technique for studying the brain in action. Since its creation [1], [2], cognitive scientists have been using fMRI to understand how we remember, manipulate, and act on information in our environment. Working with magnetic resonance physicists, statisticians, and engineers, these scientists are pushing the frontiers of knowledge of how the human brain works. The design and analysis of single-subject fMRI studies has been well described. For example, [3], chapters 10 and 11 of [4], and chapters 11 and 14 of [5] all give accessible overviews of fMRI methods for one subject. In contrast, while the appropriate manner to analyze a group of subjects has been the topic of several recent papers, we do not feel it has been covered well in introductory texts and review papers. Therefore, in this article, we bring together old and new work on so-called group modeling of fMRI data using a consistent notation to make the methods more accessible and comparable.

Item Type:	Journal Article
Subjects:	R Medicine > R Medicine (General)
Divisions:	Faculty of Science > Statistics Faculty of Science > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH):	Magnetic resonance imaging -- Mathematical models
Journal or Publication Title:	IEEE Engineering in Medicine and Biology Magazine
Publisher:	IEEE
ISSN:	0739-5175
Date:	2006
Volume:	Vol.25
Number:	No.2
Page Range:	pp. 42-51
Identification Number:	10.1109/MEMB.2006.1607668
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
References:	[1] K.K. Kwong, J.W. Belliveau, D.A. Chesler, I.E. Goldberg, R.M. Weisskoff, B.P. Poncelet, D.N. Kennedy, B.E. Hoppel, M.S. Cohen, and R. Turner, “Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation,” Proc. Nat. Acad. Sci.., vol. 89, no. 12, pp. 5675–5679, Jun. 1992. [2] S. Ogawa and T. Lee, “Functional brain mapping with physiologically sensitive image signals,” J. Magn. Reson. Imaging, vol. 2(P)-WIP (suppl.), p. S22, 1992. [3] S. Rabe-Hesketh, E.T. Bullmore, and M.J. Brammer, “The analysis of functional magnetic resonance images,” Statistical Methods Med. Res., vol. 6, no. 3, pp. 215–237, 1997. [4] R.S.J. Frackowiak, Ed., Human Brain Function., 2nd ed. New York: Academic, 2003. [5] P. Jezzard, P.M. Matthews, and S.M. Smith, Eds., Functional MRI: An Introduction to Methods. London, U.K.: Oxford Univ. Press, 2003. [6] C. Moonen and P. Bandettini, Eds., Functional MRI. New York: Springer- Verlag, 2000. [7] H. Johansen-Berg, M.F.S. Rushworth, M.D. Bogdanovic, U. Kischka, S. Wimalaratna, and P.M. Matthews, “The role of ipsilateral premotor cortex in hand movement after stroke,” Proc. Nat. Acad. Sci. U.S.A., vol. 99, no. 22, pp. 14518–14523, Oct. 2002. [8] F.A. Graybill, Theory and Application of the Linear Model. North Scituate, MA: Duxbury Press, 1976. [9] E. Zarahn, G.K. Aguirre, and M. D’Esposito, “Empirical analyses of BOLD fMRI statistics. I. Spatially unsmoothed data collected under null-hypothesis conditions,” NeuroImage, vol. 5, no. 3, pp. 179–197, Apr. 1997. [10] P.L. Purdon and R.M. Weisskoff, “Effect of temporal autocorrelation due to physiological noise and stimulus paradigm on voxel-level false-positive rates in fMRI,” Hum. Brain Map., vol. 6, no. 4, pp. 239–249, 1998. [11] J.L. Marchini and S.M. Smith, “On bias in the estimation of autocorrelations for fMRI voxel time-series analysis,” NeuroImage, vol. 18, no. 1, pp. 83–90, Jan. 2003. [12] M.W. Woolrich, B.D. Ripley, M. Brady, and S.M. Smith, “Temporal autocorrelation in univariate linear modeling of fMRI data,” NeuroImage, vol. 14, no. 6, pp. 1370–1386, Dec. 2001. [13] J.L. Marchini and B.D. Ripley, “A new statistical approach to detecting significant activation in functional MRI,” NeuroImage, vol. 12, no. 4, pp. 366–380, Oct. 2000. [14] K.J. Worsley, C.H. Liao, J. Aston, V. Petre, G.H. Duncan, F. Morales, and A.C. Evans, “A general statistical analysis for fMRI data,” NeuroImage, vol. 15, no. 1, pp. 1–15, Jan. 2002. [15] K.J. Friston, D.E. Glaser, R.N.A. Henson, S. Kiebel, C. Phillips, and J. Ashburner, “Classical and bayesian inference in neuroimaging: Applications,” NeuroImage, vol. 16, no. 2, pp. 484–512, Jun. 2002. [16] G.K. Aguirre, E. Zarahn, and M. D’Esposito, “Empirical analyses of BOLD fMRI statistics. II. Spatially smoothed data collected under null-hypothesis and experimental conditions,” NeuroImage, vol. 5, no. 3, pp. 199–212, Apr. 1997. [17] G. Verbeke and G. Molenberghs, Linear Mixed Models for Longitudinal Data. New York: Springer-Verlag, 2000. [18] C.F. Beckmann, M. Jenkinson, and S.M. Smith, “General multilevel linear modeling for group analysis in fmri,” NeuroImage, vol. 20, no. 2, pp. 1052–1063, 2003. [19] A. Holmes and K. Friston, “Generalisability, random effects and population inference,” in NeuroImage, vol. 7, p. S754, 1998. [20] K.J. Worsley, “Spatial smoothing of autocorrelations to control the degrees of freedom in fMRI analysis,” NeuroImage, vol. 26, no. 2, pp. 635–641, Jun. 2005. [21] T. Gautama and M.M. Van Hulle, “Optimal spatial regularisation of autocorrelation estimates in fMRI analysis,” NeuroImage, vol. 23, no. 3, pp. 1203–1216, Nov. 2004. [22] K.J. Friston, W. Penny, C. Phillips, S. Kiebel, G. Hinton, and J. Ashburner, “Classical and bayesian inference in neuroimaging: Theory,” NeuroImage, vol. 16, no. 2, pp. 465–483, Jun. 2002. [23] W.-L. Luo, J. Mumford, T. Wager, and T. Nichols, “Robust and Local Nonsphericity modeling for secnd level PET and fMRI analysis,” NeuroImage, vol. 22, no. 1, suppl., p. S47, 2005. [24] K.-Y. Liang and S.L. Zeger, “Longitudinal data analysis using generalized linear models,” Biometrika, vol. 73, no. 1, pp. 13–22, 1986. [25] R.P. Woods, S.T. Grafton, C.J. Holmes, S.R. Cherry, and J.C. Mazziotta, “Automated image registration: I. general methods and intrasubject, intramodality validation,” J. Comput. Aided Tomography, vol. 22, no.1, pp. 141–154, 1998. [26] K.J. Friston, J. Ashburner, C.D. Frith, J.-B. Poline, J.D. Heather, and R.S.J. Frackowiak, “Spatial registration and normalization of images,” Hum. Brain Map., vol. 3, no. 3, pp. 165–189, 1995. [27] M. Jenkinson, P. Bannister, J.M. Brady, and S.M. Smith, “Improved optimisation for the robust and accurate linear registration and motion correction of brain images,” NeuroImage, vol. 17, no. 2, pp. 825–841, 2002. [28] J. Ashburner and K.J. Friston, “Nonlinear spatial normalization using basis functions,” Hum. Brain Map., vol. 7, no. 4, pp. 254–266, 1999. [29] R.P. Woods, S.T. Grafton, J.D.G. Watson, N.L. Sicotte, and J.C. Mazziotta, “Automated image registration: Ii. intersubject validation of linear and nonlinear models,” J. Comput. Aided Tomography, vol. 22, no. 1, pp. 155–165, 1998. [30] G. Christensen, R. Rabbit, and M. Miller, “Deformable templates using large deformation kinematics,” IEEE Trans. Image Processing, vol. 5, no. 10, pp. 1435–1447, 1996. [31] P. Thompson and A. Toga, “A surface-based technique for warping 3-dimensional images of the brain,” IEEE Trans. Med. Imag., vol. 15, no. 4, pp. 1–616, 1996.
URI:	http://wrap.warwick.ac.uk/id/eprint/38356

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