Spatially adaptive subject level analyses improve random effects fMRI group studies
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[1] E. Ziegel. Permutation, Parametric, and Bootstrap Tests of Hypotheses (3rd ed.) , 2005 .
[2] Alexis Roche,et al. Improved fMRI group studies based on spatially varying non-parametric BOLD signal modeling , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[3] Mark W. Woolrich,et al. Fully Bayesian spatio-temporal modeling of FMRI data , 2004, IEEE Transactions on Medical Imaging.
[4] Alan C. Evans,et al. A general statistical analysis for fMRI data , 2000, NeuroImage.
[5] Laurent Risser,et al. Min-max Extrapolation Scheme for Fast Estimation of 3D Potts Field Partition Functions. Application to the Joint Detection-Estimation of Brain Activity in fMRI , 2011, J. Signal Process. Syst..
[6] J. Pesquet,et al. Minimization of a sparsity promoting criterion for the recovery of complex-valued signals , 2009 .
[7] Benjamin Thyreau,et al. Anatomo-Functional Description of the Brain : A Probabilistic Approach , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[8] Jean-Baptiste Poline,et al. Dealing with the shortcomings of spatial normalization: Multi‐subject parcellation of fMRI datasets , 2006, Human brain mapping.
[9] Bertrand Thirion,et al. A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI , 2008, NeuroImage.
[10] Karl J. Friston,et al. Variational Bayesian inference for fMRI time series , 2003, NeuroImage.
[11] Bertrand Thirion,et al. Robust statistics for nonparametric group analysis in fMRI , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..
[12] Mark D'Esposito,et al. Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses , 2004, NeuroImage.
[13] Laurent Risser,et al. Spatially adaptive mixture modeling for analysis of fMRI time series , 2009, NeuroImage.