178 related articles for article (PubMed ID: 34890077)
1. An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm.
Zhao W; Li H; Hao Y; Hu G; Zhang Y; Frederick BB; Cong F
Hum Brain Mapp; 2022 Apr; 43(5):1561-1576. PubMed ID: 34890077
[TBL] [Abstract][Full Text] [Related]
2. A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies.
Guo Y; Tang L
Biometrics; 2013 Dec; 69(4):970-81. PubMed ID: 24033125
[TBL] [Abstract][Full Text] [Related]
3. 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. PubMed ID: 9673671
[TBL] [Abstract][Full Text] [Related]
4. 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; 90():449-68. PubMed ID: 24389422
[TBL] [Abstract][Full Text] [Related]
5. Exploring connectivity with large-scale Granger causality on resting-state functional MRI.
DSouza AM; Abidin AZ; Leistritz L; Wismüller A
J Neurosci Methods; 2017 Aug; 287():68-79. PubMed ID: 28629720
[TBL] [Abstract][Full Text] [Related]
6. 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. PubMed ID: 17274023
[TBL] [Abstract][Full Text] [Related]
7. LEICA: Laplacian eigenmaps for group ICA decomposition of fMRI data.
Liu C; JaJa J; Pessoa L
Neuroimage; 2018 Apr; 169():363-373. PubMed ID: 29246846
[TBL] [Abstract][Full Text] [Related]
8. Source density-driven independent component analysis approach for fMRI data.
Hong B; Pearlson GD; Calhoun VD
Hum Brain Mapp; 2005 Jul; 25(3):297-307. PubMed ID: 15832316
[TBL] [Abstract][Full Text] [Related]
9. Tensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance imaging data decomposition.
Hu G; Zhang Q; Waters AB; Li H; Zhang C; Wu J; Cong F; Nickerson LD
J Neurosci Methods; 2019 Sep; 325():108359. PubMed ID: 31306718
[TBL] [Abstract][Full Text] [Related]
10. Improved FastICA algorithm in fMRI data analysis using the sparsity property of the sources.
Ge R; Wang Y; Zhang J; Yao L; Zhang H; Long Z
J Neurosci Methods; 2016 Apr; 263():103-14. PubMed ID: 26880161
[TBL] [Abstract][Full Text] [Related]
11. Fast Independent Component Analysis Algorithm-Based Functional Magnetic Resonance Imaging in the Diagnosis of Changes in Brain Functional Areas of Cerebral Infarction.
Du N; Zhang Z; Xiao Y; Jiang L
Contrast Media Mol Imaging; 2021; 2021():5177037. PubMed ID: 34912182
[TBL] [Abstract][Full Text] [Related]
12. IABC: A Toolbox for Intelligent Analysis of Brain Connectivity.
Du Y; Kong Y; He X
Neuroinformatics; 2023 Apr; 21(2):303-321. PubMed ID: 36609668
[TBL] [Abstract][Full Text] [Related]
13. Evaluation of multi-echo ICA denoising for task based fMRI studies: Block designs, rapid event-related designs, and cardiac-gated fMRI.
Gonzalez-Castillo J; Panwar P; Buchanan LC; Caballero-Gaudes C; Handwerker DA; Jangraw DC; Zachariou V; Inati S; Roopchansingh V; Derbyshire JA; Bandettini PA
Neuroimage; 2016 Nov; 141():452-468. PubMed ID: 27475290
[TBL] [Abstract][Full Text] [Related]
14. SCTICA: Sub-packet constrained temporal ICA method for fMRI data analysis.
Shi Y; Zeng W
Comput Biol Med; 2018 Nov; 102():75-85. PubMed ID: 30248514
[TBL] [Abstract][Full Text] [Related]
15. 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; 216(1):49-61. PubMed ID: 23563324
[TBL] [Abstract][Full Text] [Related]
16. 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. PubMed ID: 22898701
[TBL] [Abstract][Full Text] [Related]
17. 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; 95():232-47. PubMed ID: 24657355
[TBL] [Abstract][Full Text] [Related]
18. A group model for stable multi-subject ICA on fMRI datasets.
Varoquaux G; Sadaghiani S; Pinel P; Kleinschmidt A; Poline JB; Thirion B
Neuroimage; 2010 May; 51(1):288-99. PubMed ID: 20153834
[TBL] [Abstract][Full Text] [Related]
19. A unified framework for group independent component analysis for multi-subject fMRI data.
Guo Y; Pagnoni G
Neuroimage; 2008 Sep; 42(3):1078-93. PubMed ID: 18650105
[TBL] [Abstract][Full Text] [Related]
20. Task-driven ICA feature generation for accurate and interpretable prediction using fMRI.
Duff EP; Trachtenberg AJ; Mackay CE; Howard MA; Wilson F; Smith SM; Woolrich MW
Neuroimage; 2012 Mar; 60(1):189-203. PubMed ID: 22227050
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]