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Items: 1 to 20 of 106

1.

A robust classifier to distinguish noise from fMRI independent components.

Sochat V, Supekar K, Bustillo J, Calhoun V, Turner JA, Rubin DL.

PLoS One. 2014 Apr 18;9(4):e95493. doi: 10.1371/journal.pone.0095493. eCollection 2014.

2.

Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers.

Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM.

Neuroimage. 2014 Apr 15;90:449-68. doi: 10.1016/j.neuroimage.2013.11.046. Epub 2014 Jan 2.

3.

Automatic independent component labeling for artifact removal in fMRI.

Tohka J, Foerde K, Aron AR, Tom SM, Toga AW, Poldrack RA.

Neuroimage. 2008 Feb 1;39(3):1227-45. Epub 2007 Oct 25.

4.

A method for accurate group difference detection by constraining the mixing coefficients in an ICA framework.

Sui J, Adali T, Pearlson GD, Clark VP, Calhoun VD.

Hum Brain Mapp. 2009 Sep;30(9):2953-70. doi: 10.1002/hbm.20721.

5.

ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data.

Pruim RH, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF.

Neuroimage. 2015 May 15;112:267-77. doi: 10.1016/j.neuroimage.2015.02.064. Epub 2015 Mar 11.

PMID:
25770991
6.

Analysis of fMRI data by blind separation into independent spatial components.

McKeown MJ, Makeig S, Brown GG, Jung TP, Kindermann SS, Bell AJ, Sejnowski TJ.

Hum Brain Mapp. 1998;6(3):160-88.

PMID:
9673671
7.
8.

Denoising the speaking brain: toward a robust technique for correcting artifact-contaminated fMRI data under severe motion.

Xu Y, Tong Y, Liu S, Chow HM, AbdulSabur NY, Mattay GS, Braun AR.

Neuroimage. 2014 Dec;103:33-47. doi: 10.1016/j.neuroimage.2014.09.013. Epub 2014 Sep 16.

9.

Model-free functional MRI analysis using topographic independent component analysis.

Meyer-Bäse A, Lange O, Wismüller A, Ritter H.

Int J Neural Syst. 2004 Aug;14(4):217-28.

PMID:
15372699
10.

Application of independent component analysis with adaptive density model to complex-valued fMRI data.

Li H, Correa NM, Rodriguez PA, Calhoun VD, Adali T.

IEEE Trans Biomed Eng. 2011 Oct;58(10):2794-803. doi: 10.1109/TBME.2011.2159841. Epub 2011 Jun 16.

11.

Optimization of rs-fMRI Pre-processing for Enhanced Signal-Noise Separation, Test-Retest Reliability, and Group Discrimination.

Shirer WR, Jiang H, Price CM, Ng B, Greicius MD.

Neuroimage. 2015 Aug 15;117:67-79. doi: 10.1016/j.neuroimage.2015.05.015. Epub 2015 May 15.

PMID:
25987368
12.

ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging.

Griffanti L, Salimi-Khorshidi G, Beckmann CF, Auerbach EJ, Douaud G, Sexton CE, Zsoldos E, Ebmeier KP, Filippini N, Mackay CE, Moeller S, Xu J, Yacoub E, Baselli G, Ugurbil K, Miller KL, Smith SM.

Neuroimage. 2014 Jul 15;95:232-47. doi: 10.1016/j.neuroimage.2014.03.034. Epub 2014 Mar 21.

13.

Higher-order contrast functions improve performance of independent component analysis of fMRI data.

Schmithorst VJ.

J Magn Reson Imaging. 2009 Jan;29(1):242-9. doi: 10.1002/jmri.21621.

14.

Estimating the number of independent components for functional magnetic resonance imaging data.

Li YO, Adali T, Calhoun VD.

Hum Brain Mapp. 2007 Nov;28(11):1251-66.

PMID:
17274023
15.

Exploitation of temporal redundancy in compressed sensing reconstruction of fMRI studies with a prior-based algorithm (PICCS).

Chavarrías C, Abascal JF, Montesinos P, Desco M.

Med Phys. 2015 Jul;42(7):3814-21. doi: 10.1118/1.4921365. Erratum in: Med Phys. 2015 Aug;42(8):4997.

PMID:
26133583
16.

The non-separability of physiologic noise in functional connectivity MRI with spatial ICA at 3T.

Beall EB, Lowe MJ.

J Neurosci Methods. 2010 Aug 30;191(2):263-76. doi: 10.1016/j.jneumeth.2010.06.024. Epub 2010 Jun 30.

PMID:
20600313
17.

Spatial and temporal reproducibility-based ranking of the independent components of BOLD fMRI data.

Zeng W, Qiu A, Chodkowski B, Pekar JJ.

Neuroimage. 2009 Jul 15;46(4):1041-54. doi: 10.1016/j.neuroimage.2009.02.048. Epub 2009 Mar 12.

18.

Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI.

Pruim RH, Mennes M, Buitelaar JK, Beckmann CF.

Neuroimage. 2015 May 15;112:278-87. doi: 10.1016/j.neuroimage.2015.02.063. Epub 2015 Mar 11.

PMID:
25770990
19.

Multivariate analysis of neuronal interactions in the generalized partial least squares framework: simulations and empirical studies.

Lin FH, McIntosh AR, Agnew JA, Eden GF, Zeffiro TA, Belliveau JW.

Neuroimage. 2003 Oct;20(2):625-42.

PMID:
14568440
20.

SACICA: a sparse approximation coefficient-based ICA model for functional magnetic resonance imaging data analysis.

Wang N, Zeng W, Chen L.

J Neurosci Methods. 2013 May 30;216(1):49-61. doi: 10.1016/j.jneumeth.2013.03.014. Epub 2013 Apr 4.

PMID:
23563324

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