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Items: 1 to 20 of 185

1.

Characterization of task-free/task-performance brain states.

Zhang X, Guo L, Li X, Zhu D, Li K, Sun Z, Jin C, Hu X, Han J, Zhao Q, Li L, Liu T.

Med Image Comput Comput Assist Interv. 2012;15(Pt 2):237-45.

PMID:
23286054
2.

Characterization of task-free and task-performance brain states via functional connectome patterns.

Zhang X, Guo L, Li X, Zhang T, Zhu D, Li K, Chen H, Lv J, Jin C, Zhao Q, Li L, Liu T.

Med Image Anal. 2013 Dec;17(8):1106-22. doi: 10.1016/j.media.2013.07.003. Epub 2013 Jul 24.

3.

Sparse representation of higher-order functional interaction patterns in task-based FMRI data.

Zhang S, Li X, Lv J, Jiang X, Zhu D, Chen H, Zhang T, Guo L, Liu T.

Med Image Comput Comput Assist Interv. 2013;16(Pt 3):626-34.

PMID:
24505814
4.

Separating 4D multi-task fMRI data of multiple subjects by independent component analysis with projection.

Long Z, Li R, Wen X, Jin Z, Chen K, Yao L.

Magn Reson Imaging. 2013 Jan;31(1):60-74. doi: 10.1016/j.mri.2012.06.034. Epub 2012 Aug 13.

PMID:
22898701
5.

Optimization of fMRI-derived ROIs based on coherent functional interaction patterns.

Deng F, Zhu D, Liu T.

Med Image Comput Comput Assist Interv. 2012;15(Pt 3):214-22.

PMID:
23286133
6.

A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data.

Wu GR, Liao W, Stramaglia S, Ding JR, Chen H, Marinazzo D.

Med Image Anal. 2013 Apr;17(3):365-74. doi: 10.1016/j.media.2013.01.003. Epub 2013 Jan 29.

PMID:
23422254
7.

Resting-state FMRI single subject cortical parcellation based on region growing.

Blumensath T, Behrens TE, Smith SM.

Med Image Comput Comput Assist Interv. 2012;15(Pt 2):188-95.

PMID:
23286048
8.

Unsupervised learning of functional network dynamics in resting state fMRI.

Eavani H, Satterthwaite TD, Gur RE, Gur RC, Davatzikos C.

Inf Process Med Imaging. 2013;23:426-37.

9.

Activated fibers: fiber-centered activation detection in task-based FMRI.

Lv J, Guo L, Li K, Hu X, Zhu D, Han J, Liu T.

Inf Process Med Imaging. 2011;22:574-87.

PMID:
21761687
10.

Group analysis of resting-state fMRI by hierarchical Markov random fields.

Liu W, Awate SP, Fletcher PT.

Med Image Comput Comput Assist Interv. 2012;15(Pt 3):189-96.

11.

Brain connectivity during resting state and subsequent working memory task predicts behavioural performance.

Sala-Llonch R, Peña-Gómez C, Arenaza-Urquijo EM, Vidal-Piñeiro D, Bargalló N, Junqué C, Bartrés-Faz D.

Cortex. 2012 Oct;48(9):1187-96. doi: 10.1016/j.cortex.2011.07.006. Epub 2011 Aug 5.

PMID:
21872853
12.

Group-wise FMRI activation detection on corresponding cortical landmarks.

Lv J, Zhu D, Hu X, Zhang X, Zhang T, Han J, Guo L, Liu T.

Med Image Comput Comput Assist Interv. 2013;16(Pt 2):665-73.

PMID:
24579198
13.

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
14.

Multi-level bootstrap analysis of stable clusters in resting-state fMRI.

Bellec P, Rosa-Neto P, Lyttelton OC, Benali H, Evans AC.

Neuroimage. 2010 Jul 1;51(3):1126-39. doi: 10.1016/j.neuroimage.2010.02.082. Epub 2010 Mar 10.

PMID:
20226257
15.

The effect of scan length on the reliability of resting-state fMRI connectivity estimates.

Birn RM, Molloy EK, Patriat R, Parker T, Meier TB, Kirk GR, Nair VA, Meyerand ME, Prabhakaran V.

Neuroimage. 2013 Dec;83:550-8. doi: 10.1016/j.neuroimage.2013.05.099. Epub 2013 Jun 6.

16.

Probabilistic framework for brain connectivity from functional MR images.

Rajapakse JC, Wang Y, Zheng X, Zhou J.

IEEE Trans Med Imaging. 2008 Jun;27(6):825-33. doi: 10.1109/TMI.2008.915672.

PMID:
18541489
17.

A SVM-based quantitative fMRI method for resting-state functional network detection.

Song X, Chen NK.

Magn Reson Imaging. 2014 Sep;32(7):819-31. doi: 10.1016/j.mri.2014.04.004. Epub 2014 Apr 13.

18.

Functional connectivity during resting-state functional MR imaging: study of the correspondence between independent component analysis and region-of-interest-based methods.

Rosazza C, Minati L, Ghielmetti F, Mandelli ML, Bruzzone MG.

AJNR Am J Neuroradiol. 2012 Jan;33(1):180-7. doi: 10.3174/ajnr.A2733. Epub 2011 Oct 13.

19.
20.

Analyzing the connectivity between regions of interest: an approach based on cluster Granger causality for fMRI data analysis.

Sato JR, Fujita A, Cardoso EF, Thomaz CE, Brammer MJ, Amaro E Jr.

Neuroimage. 2010 Oct 1;52(4):1444-55. doi: 10.1016/j.neuroimage.2010.05.022. Epub 2010 Jun 1.

PMID:
20472076
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