These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

175 related articles for article (PubMed ID: 28214528)

  • 1. Adaptive independent vector analysis for multi-subject complex-valued fMRI data.
    Kuang LD; Lin QH; Gong XF; Cong F; Calhoun VD
    J Neurosci Methods; 2017 Apr; 281():49-63. PubMed ID: 28214528
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition.
    Kuang LD; Lin QH; Gong XF; Cong F; Sui J; Calhoun VD
    J Neurosci Methods; 2015 Dec; 256():127-40. PubMed ID: 26327319
    [TBL] [Abstract][Full Text] [Related]  

  • 3. ICA of full complex-valued fMRI data using phase information of spatial maps.
    Yu MC; Lin QH; Kuang LD; Gong XF; Cong F; Calhoun VD
    J Neurosci Methods; 2015 Jul; 249():75-91. PubMed ID: 25857613
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Estimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data.
    Li WX; Lin QH; Zhang CY; Han Y; Li HJ; Calhoun VD
    J Neurosci Methods; 2024 Sep; 409():110207. PubMed ID: 38944128
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint.
    Kuang LD; Lin QH; Gong XF; Cong F; Wang YP; Calhoun VD
    IEEE Trans Med Imaging; 2020 Apr; 39(4):844-853. PubMed ID: 31425066
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Sparse representation of complex-valued fMRI data based on spatiotemporal concatenation of real and imaginary parts.
    Zhang CY; Lin QH; Kuang LD; Li WX; Gong XF; Calhoun VD
    J Neurosci Methods; 2021 Mar; 351():109047. PubMed ID: 33385421
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia.
    Kuang LD; Lin QH; Gong XF; Cong F; Sui J; Calhoun VD
    J Neurosci Methods; 2018 Jul; 304():24-38. PubMed ID: 29673968
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.
    Xie J; Douglas PK; Wu YN; Brody AL; Anderson AE
    J Neurosci Methods; 2017 Apr; 282():81-94. PubMed ID: 28322859
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Independent vector analysis for common subspace analysis: Application to multi-subject fMRI data yields meaningful subgroups of schizophrenia.
    Long Q; Bhinge S; Calhoun VD; Adali T
    Neuroimage; 2020 Aug; 216():116872. PubMed ID: 32353485
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude-only fMRI data.
    Zhang CY; Lin QH; Niu YW; Li WX; Gong XF; Cong F; Wang YP; Calhoun VD
    Hum Brain Mapp; 2023 Dec; 44(17):5712-5728. PubMed ID: 37647216
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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. PubMed ID: 21690000
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Quality Map Thresholding for De-noising of Complex-Valued fMRI Data and Its Application to ICA of fMRI.
    Rodriguez PA; Correa NM; Eichele T; Calhoun VD; Adali T
    J Signal Process Syst; 2009 Sep; 2009():1-6. PubMed ID: 21949563
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Spatially regularized machine learning for task and resting-state fMRI.
    Song X; Panych LP; Chen NK
    J Neurosci Methods; 2016 Jan; 257():214-28. PubMed ID: 26470627
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A multiple kernel learning approach to perform classification of groups from complex-valued fMRI data analysis: application to schizophrenia.
    Castro E; Gómez-Verdejo V; Martínez-Ramón M; Kiehl KA; Calhoun VD
    Neuroimage; 2014 Feb; 87():1-17. PubMed ID: 24225489
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Independent component analysis in the presence of noise in fMRI.
    Cordes D; Nandy R
    Magn Reson Imaging; 2007 Nov; 25(9):1237-48. PubMed ID: 17509787
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A fully Bayesian approach for comprehensive mapping of magnitude and phase brain activation in complex-valued fMRI data.
    Wang Z; Rowe DB; Li X; Brown DA
    Magn Reson Imaging; 2024 Jun; 109():271-285. PubMed ID: 38537891
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Independent vector analysis (IVA): multivariate approach for fMRI group study.
    Lee JH; Lee TW; Jolesz FA; Yoo SS
    Neuroimage; 2008 Mar; 40(1):86-109. PubMed ID: 18165105
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A two-step super-Gaussian independent component analysis approach for fMRI data.
    Ge R; Yao L; Zhang H; Long Z
    Neuroimage; 2015 Sep; 118():344-58. PubMed ID: 26057592
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.