[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.