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Items: 1 to 20 of 106

1.

Applications of multivariate modeling to neuroimaging group analysis: a comprehensive alternative to univariate general linear model.

Chen G, Adleman NE, Saad ZS, Leibenluft E, Cox RW.

Neuroimage. 2014 Oct 1;99:571-88. doi: 10.1016/j.neuroimage.2014.06.027. Epub 2014 Jun 17.

2.

Linear mixed-effects modeling approach to FMRI group analysis.

Chen G, Saad ZS, Britton JC, Pine DS, Cox RW.

Neuroimage. 2013 Jun;73:176-90. doi: 10.1016/j.neuroimage.2013.01.047. Epub 2013 Jan 30.

3.

FMRI group analysis combining effect estimates and their variances.

Chen G, Saad ZS, Nath AR, Beauchamp MS, Cox RW.

Neuroimage. 2012 Mar;60(1):747-65. doi: 10.1016/j.neuroimage.2011.12.060. Epub 2011 Dec 30.

4.

Evaluation and comparison of GLM- and CVA-based fMRI processing pipelines with Java-based fMRI processing pipeline evaluation system.

Zhang J, Liang L, Anderson JR, Gatewood L, Rottenberg DA, Strother SC.

Neuroimage. 2008 Jul 15;41(4):1242-52. doi: 10.1016/j.neuroimage.2008.03.034. Epub 2008 Apr 3.

5.

Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA.

Zhang J, Anderson JR, Liang L, Pulapura SK, Gatewood L, Rottenberg DA, Strother SC.

Magn Reson Imaging. 2009 Feb;27(2):264-78. doi: 10.1016/j.mri.2008.05.021. Epub 2008 Oct 11.

PMID:
18849131
6.

A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

Zhang J, Liang L, Anderson JR, Gatewood L, Rottenberg DA, Strother SC.

Neuroinformatics. 2008 Summer;6(2):123-34. doi: 10.1007/s12021-008-9014-1. Epub 2008 May 28.

PMID:
18506642
7.

Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data.

Kim J, Zhu W, Chang L, Bentler PM, Ernst T.

Hum Brain Mapp. 2007 Feb;28(2):85-93.

PMID:
16718669
8.

Inter-subject correlation in fMRI: method validation against stimulus-model based analysis.

Pajula J, Kauppi JP, Tohka J.

PLoS One. 2012;7(8):e41196. doi: 10.1371/journal.pone.0041196. Epub 2012 Aug 8.

9.

What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis.

Davis T, LaRocque KF, Mumford JA, Norman KA, Wagner AD, Poldrack RA.

Neuroimage. 2014 Aug 15;97:271-83. doi: 10.1016/j.neuroimage.2014.04.037. Epub 2014 Apr 21.

10.

Detecting the subtle shape differences in hemodynamic responses at the group level.

Chen G, Saad ZS, Adleman NE, Leibenluft E, Cox RW.

Front Neurosci. 2015 Oct 26;9:375. doi: 10.3389/fnins.2015.00375. eCollection 2015.

11.

Relevant feature set estimation with a knock-out strategy and random forests.

Ganz M, Greve DN, Fischl B, Konukoglu E; Alzheimer's Disease Neuroimaging Initiative.

Neuroimage. 2015 Nov 15;122:131-48. doi: 10.1016/j.neuroimage.2015.08.006. Epub 2015 Aug 10.

12.

Estimating and testing variance components in a multi-level GLM.

Lindquist MA, Spicer J, Asllani I, Wager TD.

Neuroimage. 2012 Jan 2;59(1):490-501. doi: 10.1016/j.neuroimage.2011.07.077. Epub 2011 Jul 31.

13.

Support vector machine learning-based fMRI data group analysis.

Wang Z, Childress AR, Wang J, Detre JA.

Neuroimage. 2007 Jul 15;36(4):1139-51. Epub 2007 Apr 27.

14.

Penalized likelihood phenotyping: unifying voxelwise analyses and multi-voxel pattern analyses in neuroimaging: penalized likelihood phenotyping.

Adluru N, Hanlon BM, Lutz A, Lainhart JE, Alexander AL, Davidson RJ.

Neuroinformatics. 2013 Apr;11(2):227-47. doi: 10.1007/s12021-012-9175-9.

15.

Recommendations for analysis of repeated-measures designs: testing and correcting for sphericity and use of manova and mixed model analysis.

Armstrong RA.

Ophthalmic Physiol Opt. 2017 Sep;37(5):585-593. doi: 10.1111/opo.12399. Epub 2017 Jul 20. Review.

PMID:
28726257
16.

Modeling state-related fMRI activity using change-point theory.

Lindquist MA, Waugh C, Wager TD.

Neuroimage. 2007 Apr 15;35(3):1125-41. Epub 2007 Jan 23.

PMID:
17360198
17.

Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data.

Drakesmith M, Caeyenberghs K, Dutt A, Lewis G, David AS, Jones DK.

Neuroimage. 2015 Sep;118:313-33. doi: 10.1016/j.neuroimage.2015.05.011. Epub 2015 May 14.

18.

A comment on the severity of the effects of non-white noise in fMRI time-series.

Smith AT, Singh KD, Balsters JH.

Neuroimage. 2007 Jun;36(2):282-8. Epub 2006 Nov 13.

PMID:
17098446
19.

Estimating brain network activity through back-projection of ICA components to GLM maps.

James GA, Tripathi SP, Kilts CD.

Neurosci Lett. 2014 Apr 3;564:21-6. doi: 10.1016/j.neulet.2014.01.056. Epub 2014 Feb 7.

20.

Advantages and disadvantages of a fast fMRI sequence in the context of EEG-fMRI investigation of epilepsy patients: A realistic simulation study.

Safi-Harb M, Proulx S, von Ellenrieder N, Gotman J.

Neuroimage. 2015 Oct 1;119:20-32. doi: 10.1016/j.neuroimage.2015.06.039. Epub 2015 Jun 18.

PMID:
26093328

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